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Glossary of VSA attributes
This Glossary alphabetically lists all attributes used in the VSAv20241206 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20241206 Schema Browser (other Browser versions).

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

J
Name	Schema Table	Database	Description	Type	Length	Unit	Default Value	Unified Content Descriptor
J	twomass	SIXDF	J magnitude (JEXT) used for J selection	real	4	mag
j_1AperMag1	vvvSource	VVVDR5	Point source J_1 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_1AperMag1	vvvxSource	VVVXDR1	Point source J_1 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_1AperMag1Err	vvvSource	VVVDR5	Error in point source J_1 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_1AperMag1Err	vvvxSource	VVVXDR1	Error in point source J_1 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_1AperMag3	vikingSource	VIKINGv20151230	Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag3	vikingSource	VIKINGv20160406	Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag3	vikingSource	VIKINGv20161202	Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag3	vikingSource	VIKINGv20170715	Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag3	vvvSource	VVVDR5	Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_1AperMag3	vvvxSource	VVVXDR1	Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_1AperMag3Err	vikingSource	VIKINGv20151230	Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag3Err	vikingSource	VIKINGv20160406	Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag3Err	vikingSource	VIKINGv20161202	Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag3Err	vikingSource	VIKINGv20170715	Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag3Err	vvvSource	VVVDR5	Error in default point source J_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_1AperMag3Err	vvvxSource	VVVXDR1	Error in default point source J_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_1AperMag4	vikingSource	VIKINGv20151230	Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag4	vikingSource	VIKINGv20160406	Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag4	vikingSource	VIKINGv20161202	Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag4	vikingSource	VIKINGv20170715	Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag4	vvvSource	VVVDR5	Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_1AperMag4	vvvxSource	VVVXDR1	Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_1AperMag4Err	vikingSource	VIKINGv20151230	Error in point/extended source J_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag4Err	vikingSource	VIKINGv20160406	Error in point/extended source J_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag4Err	vikingSource	VIKINGv20161202	Error in point/extended source J_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag4Err	vikingSource	VIKINGv20170715	Error in point/extended source J_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag4Err	vvvSource	VVVDR5	Error in point source J_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_1AperMag4Err	vvvxSource	VVVXDR1	Error in point source J_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_1AperMag6	vikingSource	VIKINGv20151230	Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag6	vikingSource	VIKINGv20160406	Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag6	vikingSource	VIKINGv20161202	Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag6	vikingSource	VIKINGv20170715	Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMag6Err	vikingSource	VIKINGv20151230	Error in point/extended source J_1 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag6Err	vikingSource	VIKINGv20160406	Error in point/extended source J_1 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag6Err	vikingSource	VIKINGv20161202	Error in point/extended source J_1 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMag6Err	vikingSource	VIKINGv20170715	Error in point/extended source J_1 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1AperMagNoAperCorr3	vikingSource	VIKINGv20151230	Default extended source J_1 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr3	vikingSource	VIKINGv20160406	Default extended source J_1 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr3	vikingSource	VIKINGv20161202	Default extended source J_1 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr3	vikingSource	VIKINGv20170715	Default extended source J_1 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr4	vikingSource	VIKINGv20151230	Extended source J_1 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr4	vikingSource	VIKINGv20160406	Extended source J_1 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr4	vikingSource	VIKINGv20161202	Extended source J_1 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr4	vikingSource	VIKINGv20170715	Extended source J_1 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr6	vikingSource	VIKINGv20151230	Extended source J_1 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr6	vikingSource	VIKINGv20160406	Extended source J_1 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr6	vikingSource	VIKINGv20161202	Extended source J_1 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AperMagNoAperCorr6	vikingSource	VIKINGv20170715	Extended source J_1 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_1AverageConf	vikingSource	VIKINGv20151230	average confidence in 2 arcsec diameter default aperture (aper3) J_1	real	4		-0.9999995e9	stat.likelihood
j_1AverageConf	vikingSource	VIKINGv20160406	average confidence in 2 arcsec diameter default aperture (aper3) J_1	real	4		-0.9999995e9	stat.likelihood
j_1AverageConf	vikingSource	VIKINGv20161202	average confidence in 2 arcsec diameter default aperture (aper3) J_1	real	4		-0.9999995e9	stat.likelihood
j_1AverageConf	vikingSource	VIKINGv20170715	average confidence in 2 arcsec diameter default aperture (aper3) J_1	real	4		-0.9999995e9	stat.likelihood
j_1AverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) J_1	real	4		-0.9999995e9	stat.likelihood;em.IR.J
j_1AverageConf	vvvxSource	VVVXDR1	average confidence in 2 arcsec diameter default aperture (aper3) J_1	real	4		-0.9999995e9	stat.likelihood;em.IR.J
j_1Class	vikingSource	VIKINGv20151230	discrete image classification flag in J_1	smallint	2		-9999	src.class
j_1Class	vikingSource	VIKINGv20160406	discrete image classification flag in J_1	smallint	2		-9999	src.class
j_1Class	vikingSource	VIKINGv20161202	discrete image classification flag in J_1	smallint	2		-9999	src.class
j_1Class	vikingSource	VIKINGv20170715	discrete image classification flag in J_1	smallint	2		-9999	src.class
j_1Class	vvvSource	VVVDR5	discrete image classification flag in J_1	smallint	2		-9999	src.class;em.IR.J
j_1Class	vvvxSource	VVVXDR1	discrete image classification flag in J_1	smallint	2		-9999	src.class;em.IR.J
j_1ClassStat	vikingSource	VIKINGv20151230	N(0,1) stellarness-of-profile statistic in J_1	real	4		-0.9999995e9	stat
j_1ClassStat	vikingSource	VIKINGv20160406	N(0,1) stellarness-of-profile statistic in J_1	real	4		-0.9999995e9	stat
j_1ClassStat	vikingSource	VIKINGv20161202	N(0,1) stellarness-of-profile statistic in J_1	real	4		-0.9999995e9	stat
j_1ClassStat	vikingSource	VIKINGv20170715	N(0,1) stellarness-of-profile statistic in J_1	real	4		-0.9999995e9	stat
j_1ClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in J_1	real	4		-0.9999995e9	stat;em.IR.J
j_1ClassStat	vvvxSource	VVVXDR1	S-Extractor classification statistic in J_1	real	4		-0.9999995e9	stat;em.IR.J
j_1Ell	vikingSource	VIKINGv20151230	1-b/a, where a/b=semi-major/minor axes in J_1	real	4		-0.9999995e9	src.ellipticity
j_1Ell	vikingSource	VIKINGv20160406	1-b/a, where a/b=semi-major/minor axes in J_1	real	4		-0.9999995e9	src.ellipticity
j_1Ell	vikingSource	VIKINGv20161202	1-b/a, where a/b=semi-major/minor axes in J_1	real	4		-0.9999995e9	src.ellipticity
j_1Ell	vikingSource	VIKINGv20170715	1-b/a, where a/b=semi-major/minor axes in J_1	real	4		-0.9999995e9	src.ellipticity
j_1Ell	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in J_1	real	4		-0.9999995e9	src.ellipticity;em.IR.J
j_1Ell	vvvxSource	VVVXDR1	1-b/a, where a/b=semi-major/minor axes in J_1	real	4		-0.9999995e9	src.ellipticity;em.IR.J
j_1eNum	vikingMergeLog	VIKINGv20151230	the extension number of this J_1 frame	tinyint	1			meta.number
j_1eNum	vikingMergeLog	VIKINGv20160406	the extension number of this J_1 frame	tinyint	1			meta.number
j_1eNum	vikingMergeLog	VIKINGv20161202	the extension number of this J_1 frame	tinyint	1			meta.number
j_1eNum	vikingMergeLog	VIKINGv20170715	the extension number of this J_1 frame	tinyint	1			meta.number
j_1eNum	vvvMergeLog	VVVDR5	the extension number of this J_1 frame	tinyint	1			meta.number;em.IR.J
j_1eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the extension number of this 1st epoch J frame	tinyint	1			meta.number;em.IR.J
j_1eNum	vvvxMergeLog	VVVXDR1	the extension number of this J_1 frame	tinyint	1			meta.id;em.IR.J
j_1ErrBits	vikingSource	VIKINGv20151230	processing warning/error bitwise flags in J_1	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits	vikingSource	VIKINGv20160406	processing warning/error bitwise flags in J_1	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits	vikingSource	VIKINGv20161202	processing warning/error bitwise flags in J_1	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits	vikingSource	VIKINGv20170715	processing warning/error bitwise flags in J_1	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in J_1	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits	vvvxSource	VVVXDR1	processing warning/error bitwise flags in J_1	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1Eta	vikingSource	VIKINGv20151230	Offset of J_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta	vikingSource	VIKINGv20160406	Offset of J_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta	vikingSource	VIKINGv20161202	Offset of J_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta	vikingSource	VIKINGv20170715	Offset of J_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta	vvvSource	VVVDR5	Offset of J_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta	vvvxSource	VVVXDR1	Offset of J_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Gausig	vikingSource	VIKINGv20151230	RMS of axes of ellipse fit in J_1	real	4	pixels	-0.9999995e9	src.morph.param
j_1Gausig	vikingSource	VIKINGv20160406	RMS of axes of ellipse fit in J_1	real	4	pixels	-0.9999995e9	src.morph.param
j_1Gausig	vikingSource	VIKINGv20161202	RMS of axes of ellipse fit in J_1	real	4	pixels	-0.9999995e9	src.morph.param
j_1Gausig	vikingSource	VIKINGv20170715	RMS of axes of ellipse fit in J_1	real	4	pixels	-0.9999995e9	src.morph.param
j_1Gausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in J_1	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
j_1Gausig	vvvxSource	VVVXDR1	RMS of axes of ellipse fit in J_1	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
j_1HlCorSMjRadAs	vikingSource	VIKINGv20151230	Seeing corrected half-light, semi-major axis in J_1 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_1HlCorSMjRadAs	vikingSource	VIKINGv20160406	Seeing corrected half-light, semi-major axis in J_1 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_1HlCorSMjRadAs	vikingSource	VIKINGv20161202	Seeing corrected half-light, semi-major axis in J_1 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_1HlCorSMjRadAs	vikingSource	VIKINGv20170715	Seeing corrected half-light, semi-major axis in J_1 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_1mfID	vikingMergeLog	VIKINGv20151230	the UID of the relevant J_1 multiframe	bigint	8			meta.id;obs.field
j_1mfID	vikingMergeLog	VIKINGv20160406	the UID of the relevant J_1 multiframe	bigint	8			meta.id;obs.field
j_1mfID	vikingMergeLog	VIKINGv20161202	the UID of the relevant J_1 multiframe	bigint	8			meta.id;obs.field
j_1mfID	vikingMergeLog	VIKINGv20170715	the UID of the relevant J_1 multiframe	bigint	8			meta.id;obs.field
j_1mfID	vvvMergeLog	VVVDR5	the UID of the relevant J_1 multiframe	bigint	8			meta.id;obs.field;em.IR.J
j_1mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the UID of the relevant 1st epoch J tile multiframe	bigint	8			meta.id;obs.field;em.IR.J
j_1mfID	vvvxMergeLog	VVVXDR1	the UID of the relevant J_1 multiframe	bigint	8			meta.id;obs.field;em.IR.J
j_1mh_1Pnt	vvvSource	VVVDR5	Point source colour J_1-H_1 (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mh_1PntErr	vvvSource	VVVDR5	Error on point source colour J_1-H_1	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt	vikingSource	VIKINGv20151230	Extended source colour J_1-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt	vikingSource	VIKINGv20160406	Extended source colour J_1-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt	vikingSource	VIKINGv20161202	Extended source colour J_1-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt	vikingSource	VIKINGv20170715	Extended source colour J_1-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr	vikingSource	VIKINGv20151230	Error on extended source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr	vikingSource	VIKINGv20160406	Error on extended source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr	vikingSource	VIKINGv20161202	Error on extended source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr	vikingSource	VIKINGv20170715	Error on extended source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt	vikingSource	VIKINGv20151230	Point source colour J_1-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt	vikingSource	VIKINGv20160406	Point source colour J_1-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt	vikingSource	VIKINGv20161202	Point source colour J_1-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt	vikingSource	VIKINGv20170715	Point source colour J_1-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt	vvvxSource	VVVXDR1	Point source colour J_1-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr	vikingSource	VIKINGv20151230	Error on point source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr	vikingSource	VIKINGv20160406	Error on point source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr	vikingSource	VIKINGv20161202	Error on point source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr	vikingSource	VIKINGv20170715	Error on point source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr	vvvxSource	VVVXDR1	Error on point source colour J_1-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1Mjd	vikingSource	VIKINGv20151230	Modified Julian Day in J_1 band	float	8	days	-0.9999995e9	time.epoch
j_1Mjd	vikingSource	VIKINGv20160406	Modified Julian Day in J_1 band	float	8	days	-0.9999995e9	time.epoch
j_1Mjd	vikingSource	VIKINGv20161202	Modified Julian Day in J_1 band	float	8	days	-0.9999995e9	time.epoch
j_1Mjd	vikingSource	VIKINGv20170715	Modified Julian Day in J_1 band	float	8	days	-0.9999995e9	time.epoch
j_1Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the MJD of the 1st epoch J tile multiframe	float	8			time;em.IR.J
j_1Mjd	vvvSource	VVVDR5	Modified Julian Day in J_1 band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
j_1Mjd	vvvxSource	VVVXDR1	Modified Julian Day in J_1 band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
j_1PA	vikingSource	VIKINGv20151230	ellipse fit celestial orientation in J_1	real	4	Degrees	-0.9999995e9	pos.posAng
j_1PA	vikingSource	VIKINGv20160406	ellipse fit celestial orientation in J_1	real	4	Degrees	-0.9999995e9	pos.posAng
j_1PA	vikingSource	VIKINGv20161202	ellipse fit celestial orientation in J_1	real	4	Degrees	-0.9999995e9	pos.posAng
j_1PA	vikingSource	VIKINGv20170715	ellipse fit celestial orientation in J_1	real	4	Degrees	-0.9999995e9	pos.posAng
j_1PA	vvvSource	VVVDR5	ellipse fit celestial orientation in J_1	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
j_1PA	vvvxSource	VVVXDR1	ellipse fit celestial orientation in J_1	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
j_1PetroMag	vikingSource	VIKINGv20151230	Extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_1PetroMag	vikingSource	VIKINGv20160406	Extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_1PetroMag	vikingSource	VIKINGv20161202	Extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_1PetroMag	vikingSource	VIKINGv20170715	Extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_1PetroMagErr	vikingSource	VIKINGv20151230	Error in extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1PetroMagErr	vikingSource	VIKINGv20160406	Error in extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1PetroMagErr	vikingSource	VIKINGv20161202	Error in extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1PetroMagErr	vikingSource	VIKINGv20170715	Error in extended source J_1 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1ppErrBits	vikingSource	VIKINGv20151230	additional WFAU post-processing error bits in J_1	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits	vikingSource	VIKINGv20160406	additional WFAU post-processing error bits in J_1	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits	vikingSource	VIKINGv20161202	additional WFAU post-processing error bits in J_1	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits	vikingSource	VIKINGv20170715	additional WFAU post-processing error bits in J_1	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in J_1	int	4		0	meta.code;em.IR.J
j_1ppErrBits	vvvxSource	VVVXDR1	additional WFAU post-processing error bits in J_1	int	4		0	meta.code;em.IR.J
j_1PsfMag	vikingSource	VIKINGv20151230	Point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	phot.mag
j_1PsfMag	vikingSource	VIKINGv20160406	Point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	phot.mag
j_1PsfMag	vikingSource	VIKINGv20161202	Point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	phot.mag
j_1PsfMag	vikingSource	VIKINGv20170715	Point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	phot.mag
j_1PsfMagErr	vikingSource	VIKINGv20151230	Error in point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1PsfMagErr	vikingSource	VIKINGv20160406	Error in point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1PsfMagErr	vikingSource	VIKINGv20161202	Error in point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1PsfMagErr	vikingSource	VIKINGv20170715	Error in point source profile-fitted J_1 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1SeqNum	vikingSource	VIKINGv20151230	the running number of the J_1 detection	int	4		-99999999	meta.number
j_1SeqNum	vikingSource	VIKINGv20160406	the running number of the J_1 detection	int	4		-99999999	meta.number
j_1SeqNum	vikingSource	VIKINGv20161202	the running number of the J_1 detection	int	4		-99999999	meta.number
j_1SeqNum	vikingSource	VIKINGv20170715	the running number of the J_1 detection	int	4		-99999999	meta.number
j_1SeqNum	vvvSource	VVVDR5	the running number of the J_1 detection	int	4		-99999999	meta.number;em.IR.J
j_1SeqNum	vvvxSource	VVVXDR1	the running number of the J_1 detection	int	4		-99999999	meta.id;em.IR.J
j_1SerMag2D	vikingSource	VIKINGv20151230	Extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_1SerMag2D	vikingSource	VIKINGv20160406	Extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_1SerMag2D	vikingSource	VIKINGv20161202	Extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_1SerMag2D	vikingSource	VIKINGv20170715	Extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_1SerMag2DErr	vikingSource	VIKINGv20151230	Error in extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1SerMag2DErr	vikingSource	VIKINGv20160406	Error in extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1SerMag2DErr	vikingSource	VIKINGv20161202	Error in extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1SerMag2DErr	vikingSource	VIKINGv20170715	Error in extended source J_1 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_1Xi	vikingSource	VIKINGv20151230	Offset of J_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi	vikingSource	VIKINGv20160406	Offset of J_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi	vikingSource	VIKINGv20161202	Offset of J_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi	vikingSource	VIKINGv20170715	Offset of J_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi	vvvSource	VVVDR5	Offset of J_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi	vvvxSource	VVVXDR1	Offset of J_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2AperMag1	vvvSource	VVVDR5	Point source J_2 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_2AperMag1	vvvxSource	VVVXDR1	Point source J_2 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_2AperMag1Err	vvvSource	VVVDR5	Error in point source J_2 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_2AperMag1Err	vvvxSource	VVVXDR1	Error in point source J_2 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_2AperMag3	vikingSource	VIKINGv20151230	Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag3	vikingSource	VIKINGv20160406	Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag3	vikingSource	VIKINGv20161202	Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag3	vikingSource	VIKINGv20170715	Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag3	vvvSource	VVVDR5	Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_2AperMag3	vvvxSource	VVVXDR1	Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_2AperMag3Err	vikingSource	VIKINGv20151230	Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag3Err	vikingSource	VIKINGv20160406	Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag3Err	vikingSource	VIKINGv20161202	Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag3Err	vikingSource	VIKINGv20170715	Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag3Err	vvvSource	VVVDR5	Error in default point source J_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_2AperMag3Err	vvvxSource	VVVXDR1	Error in default point source J_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_2AperMag4	vikingSource	VIKINGv20151230	Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag4	vikingSource	VIKINGv20160406	Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag4	vikingSource	VIKINGv20161202	Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag4	vikingSource	VIKINGv20170715	Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag4	vvvSource	VVVDR5	Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_2AperMag4	vvvxSource	VVVXDR1	Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
j_2AperMag4Err	vikingSource	VIKINGv20151230	Error in point/extended source J_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag4Err	vikingSource	VIKINGv20160406	Error in point/extended source J_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag4Err	vikingSource	VIKINGv20161202	Error in point/extended source J_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag4Err	vikingSource	VIKINGv20170715	Error in point/extended source J_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag4Err	vvvSource	VVVDR5	Error in point source J_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_2AperMag4Err	vvvxSource	VVVXDR1	Error in point source J_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
j_2AperMag6	vikingSource	VIKINGv20151230	Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag6	vikingSource	VIKINGv20160406	Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag6	vikingSource	VIKINGv20161202	Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag6	vikingSource	VIKINGv20170715	Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMag6Err	vikingSource	VIKINGv20151230	Error in point/extended source J_2 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag6Err	vikingSource	VIKINGv20160406	Error in point/extended source J_2 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag6Err	vikingSource	VIKINGv20161202	Error in point/extended source J_2 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMag6Err	vikingSource	VIKINGv20170715	Error in point/extended source J_2 mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2AperMagNoAperCorr3	vikingSource	VIKINGv20151230	Default extended source J_2 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr3	vikingSource	VIKINGv20160406	Default extended source J_2 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr3	vikingSource	VIKINGv20161202	Default extended source J_2 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr3	vikingSource	VIKINGv20170715	Default extended source J_2 aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr4	vikingSource	VIKINGv20151230	Extended source J_2 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr4	vikingSource	VIKINGv20160406	Extended source J_2 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr4	vikingSource	VIKINGv20161202	Extended source J_2 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr4	vikingSource	VIKINGv20170715	Extended source J_2 aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr6	vikingSource	VIKINGv20151230	Extended source J_2 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr6	vikingSource	VIKINGv20160406	Extended source J_2 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr6	vikingSource	VIKINGv20161202	Extended source J_2 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AperMagNoAperCorr6	vikingSource	VIKINGv20170715	Extended source J_2 aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
j_2AverageConf	vikingSource	VIKINGv20151230	average confidence in 2 arcsec diameter default aperture (aper3) J_2	real	4		-0.9999995e9	stat.likelihood
j_2AverageConf	vikingSource	VIKINGv20160406	average confidence in 2 arcsec diameter default aperture (aper3) J_2	real	4		-0.9999995e9	stat.likelihood
j_2AverageConf	vikingSource	VIKINGv20161202	average confidence in 2 arcsec diameter default aperture (aper3) J_2	real	4		-0.9999995e9	stat.likelihood
j_2AverageConf	vikingSource	VIKINGv20170715	average confidence in 2 arcsec diameter default aperture (aper3) J_2	real	4		-0.9999995e9	stat.likelihood
j_2AverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) J_2	real	4		-0.9999995e9	stat.likelihood;em.IR.J
j_2AverageConf	vvvxSource	VVVXDR1	average confidence in 2 arcsec diameter default aperture (aper3) J_2	real	4		-0.9999995e9	stat.likelihood;em.IR.J
j_2Class	vikingSource	VIKINGv20151230	discrete image classification flag in J_2	smallint	2		-9999	src.class
j_2Class	vikingSource	VIKINGv20160406	discrete image classification flag in J_2	smallint	2		-9999	src.class
j_2Class	vikingSource	VIKINGv20161202	discrete image classification flag in J_2	smallint	2		-9999	src.class
j_2Class	vikingSource	VIKINGv20170715	discrete image classification flag in J_2	smallint	2		-9999	src.class
j_2Class	vvvSource	VVVDR5	discrete image classification flag in J_2	smallint	2		-9999	src.class;em.IR.J
j_2Class	vvvxSource	VVVXDR1	discrete image classification flag in J_2	smallint	2		-9999	src.class;em.IR.J
j_2ClassStat	vikingSource	VIKINGv20151230	N(0,1) stellarness-of-profile statistic in J_2	real	4		-0.9999995e9	stat
j_2ClassStat	vikingSource	VIKINGv20160406	N(0,1) stellarness-of-profile statistic in J_2	real	4		-0.9999995e9	stat
j_2ClassStat	vikingSource	VIKINGv20161202	N(0,1) stellarness-of-profile statistic in J_2	real	4		-0.9999995e9	stat
j_2ClassStat	vikingSource	VIKINGv20170715	N(0,1) stellarness-of-profile statistic in J_2	real	4		-0.9999995e9	stat
j_2ClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in J_2	real	4		-0.9999995e9	stat;em.IR.J
j_2ClassStat	vvvxSource	VVVXDR1	S-Extractor classification statistic in J_2	real	4		-0.9999995e9	stat;em.IR.J
j_2Ell	vikingSource	VIKINGv20151230	1-b/a, where a/b=semi-major/minor axes in J_2	real	4		-0.9999995e9	src.ellipticity
j_2Ell	vikingSource	VIKINGv20160406	1-b/a, where a/b=semi-major/minor axes in J_2	real	4		-0.9999995e9	src.ellipticity
j_2Ell	vikingSource	VIKINGv20161202	1-b/a, where a/b=semi-major/minor axes in J_2	real	4		-0.9999995e9	src.ellipticity
j_2Ell	vikingSource	VIKINGv20170715	1-b/a, where a/b=semi-major/minor axes in J_2	real	4		-0.9999995e9	src.ellipticity
j_2Ell	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in J_2	real	4		-0.9999995e9	src.ellipticity;em.IR.J
j_2Ell	vvvxSource	VVVXDR1	1-b/a, where a/b=semi-major/minor axes in J_2	real	4		-0.9999995e9	src.ellipticity;em.IR.J
j_2eNum	vikingMergeLog	VIKINGv20151230	the extension number of this J_2 frame	tinyint	1			meta.number
j_2eNum	vikingMergeLog	VIKINGv20160406	the extension number of this J_2 frame	tinyint	1			meta.number
j_2eNum	vikingMergeLog	VIKINGv20161202	the extension number of this J_2 frame	tinyint	1			meta.number
j_2eNum	vikingMergeLog	VIKINGv20170715	the extension number of this J_2 frame	tinyint	1			meta.number
j_2eNum	vvvMergeLog	VVVDR5	the extension number of this J_2 frame	tinyint	1			meta.number;em.IR.J
j_2eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the extension number of this 2nd epoch J frame	tinyint	1			meta.number;em.IR.J
j_2eNum	vvvxMergeLog	VVVXDR1	the extension number of this J_2 frame	tinyint	1			meta.id;em.IR.J
j_2ErrBits	vikingSource	VIKINGv20151230	processing warning/error bitwise flags in J_2	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits	vikingSource	VIKINGv20160406	processing warning/error bitwise flags in J_2	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits	vikingSource	VIKINGv20161202	processing warning/error bitwise flags in J_2	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits	vikingSource	VIKINGv20170715	processing warning/error bitwise flags in J_2	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in J_2	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits	vvvxSource	VVVXDR1	processing warning/error bitwise flags in J_2	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2Eta	vikingSource	VIKINGv20151230	Offset of J_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta	vikingSource	VIKINGv20160406	Offset of J_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta	vikingSource	VIKINGv20161202	Offset of J_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta	vikingSource	VIKINGv20170715	Offset of J_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta	vvvSource	VVVDR5	Offset of J_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta	vvvxSource	VVVXDR1	Offset of J_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Gausig	vikingSource	VIKINGv20151230	RMS of axes of ellipse fit in J_2	real	4	pixels	-0.9999995e9	src.morph.param
j_2Gausig	vikingSource	VIKINGv20160406	RMS of axes of ellipse fit in J_2	real	4	pixels	-0.9999995e9	src.morph.param
j_2Gausig	vikingSource	VIKINGv20161202	RMS of axes of ellipse fit in J_2	real	4	pixels	-0.9999995e9	src.morph.param
j_2Gausig	vikingSource	VIKINGv20170715	RMS of axes of ellipse fit in J_2	real	4	pixels	-0.9999995e9	src.morph.param
j_2Gausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in J_2	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
j_2Gausig	vvvxSource	VVVXDR1	RMS of axes of ellipse fit in J_2	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
j_2HlCorSMjRadAs	vikingSource	VIKINGv20151230	Seeing corrected half-light, semi-major axis in J_2 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_2HlCorSMjRadAs	vikingSource	VIKINGv20160406	Seeing corrected half-light, semi-major axis in J_2 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_2HlCorSMjRadAs	vikingSource	VIKINGv20161202	Seeing corrected half-light, semi-major axis in J_2 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_2HlCorSMjRadAs	vikingSource	VIKINGv20170715	Seeing corrected half-light, semi-major axis in J_2 band	real	4	arcsec	-0.9999995e9	phys.angSize
j_2mfID	vikingMergeLog	VIKINGv20151230	the UID of the relevant J_2 multiframe	bigint	8			meta.id;obs.field
j_2mfID	vikingMergeLog	VIKINGv20160406	the UID of the relevant J_2 multiframe	bigint	8			meta.id;obs.field
j_2mfID	vikingMergeLog	VIKINGv20161202	the UID of the relevant J_2 multiframe	bigint	8			meta.id;obs.field
j_2mfID	vikingMergeLog	VIKINGv20170715	the UID of the relevant J_2 multiframe	bigint	8			meta.id;obs.field
j_2mfID	vvvMergeLog	VVVDR5	the UID of the relevant J_2 multiframe	bigint	8			meta.id;obs.field;em.IR.J
j_2mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the UID of the relevant 2nd epoch J tile multiframe	bigint	8			meta.id;obs.field;em.IR.J
j_2mfID	vvvxMergeLog	VVVXDR1	the UID of the relevant J_2 multiframe	bigint	8			meta.id;obs.field;em.IR.J
j_2mh_2Pnt	vvvSource	VVVDR5	Point source colour J_2-H_2 (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_2mh_2PntErr	vvvSource	VVVDR5	Error on point source colour J_2-H_2	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_2mhPnt	vvvxSource	VVVXDR1	Point source colour J_2-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_2mhPntErr	vvvxSource	VVVXDR1	Error on point source colour J_2-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_2Mjd	vikingSource	VIKINGv20151230	Modified Julian Day in J_2 band	float	8	days	-0.9999995e9	time.epoch
j_2Mjd	vikingSource	VIKINGv20160406	Modified Julian Day in J_2 band	float	8	days	-0.9999995e9	time.epoch
j_2Mjd	vikingSource	VIKINGv20161202	Modified Julian Day in J_2 band	float	8	days	-0.9999995e9	time.epoch
j_2Mjd	vikingSource	VIKINGv20170715	Modified Julian Day in J_2 band	float	8	days	-0.9999995e9	time.epoch
j_2Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the MJD of the 2nd epoch J tile multiframe	float	8			time;em.IR.J
j_2Mjd	vvvSource	VVVDR5	Modified Julian Day in J_2 band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
j_2Mjd	vvvxSource	VVVXDR1	Modified Julian Day in J_2 band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
j_2mrat	twomass_scn	TWOMASS	J-band average 2nd image moment ratio.	real	4			stat.fit.param
j_2mrat	twomass_sixx2_scn	TWOMASS	J band average 2nd image moment ratio for scan	real	4
j_2PA	vikingSource	VIKINGv20151230	ellipse fit celestial orientation in J_2	real	4	Degrees	-0.9999995e9	pos.posAng
j_2PA	vikingSource	VIKINGv20160406	ellipse fit celestial orientation in J_2	real	4	Degrees	-0.9999995e9	pos.posAng
j_2PA	vikingSource	VIKINGv20161202	ellipse fit celestial orientation in J_2	real	4	Degrees	-0.9999995e9	pos.posAng
j_2PA	vikingSource	VIKINGv20170715	ellipse fit celestial orientation in J_2	real	4	Degrees	-0.9999995e9	pos.posAng
j_2PA	vvvSource	VVVDR5	ellipse fit celestial orientation in J_2	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
j_2PA	vvvxSource	VVVXDR1	ellipse fit celestial orientation in J_2	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
j_2PetroMag	vikingSource	VIKINGv20151230	Extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_2PetroMag	vikingSource	VIKINGv20160406	Extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_2PetroMag	vikingSource	VIKINGv20161202	Extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_2PetroMag	vikingSource	VIKINGv20170715	Extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
j_2PetroMagErr	vikingSource	VIKINGv20151230	Error in extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2PetroMagErr	vikingSource	VIKINGv20160406	Error in extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2PetroMagErr	vikingSource	VIKINGv20161202	Error in extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2PetroMagErr	vikingSource	VIKINGv20170715	Error in extended source J_2 mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2ppErrBits	vikingSource	VIKINGv20151230	additional WFAU post-processing error bits in J_2	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits	vikingSource	VIKINGv20160406	additional WFAU post-processing error bits in J_2	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits	vikingSource	VIKINGv20161202	additional WFAU post-processing error bits in J_2	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits	vikingSource	VIKINGv20170715	additional WFAU post-processing error bits in J_2	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in J_2	int	4		0	meta.code;em.IR.J
j_2ppErrBits	vvvxSource	VVVXDR1	additional WFAU post-processing error bits in J_2	int	4		0	meta.code;em.IR.J
j_2PsfMag	vikingSource	VIKINGv20151230	Point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	phot.mag
j_2PsfMag	vikingSource	VIKINGv20160406	Point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	phot.mag
j_2PsfMag	vikingSource	VIKINGv20161202	Point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	phot.mag
j_2PsfMag	vikingSource	VIKINGv20170715	Point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	phot.mag
j_2PsfMagErr	vikingSource	VIKINGv20151230	Error in point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2PsfMagErr	vikingSource	VIKINGv20160406	Error in point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2PsfMagErr	vikingSource	VIKINGv20161202	Error in point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2PsfMagErr	vikingSource	VIKINGv20170715	Error in point source profile-fitted J_2 mag	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2SeqNum	vikingSource	VIKINGv20151230	the running number of the J_2 detection	int	4		-99999999	meta.number
j_2SeqNum	vikingSource	VIKINGv20160406	the running number of the J_2 detection	int	4		-99999999	meta.number
j_2SeqNum	vikingSource	VIKINGv20161202	the running number of the J_2 detection	int	4		-99999999	meta.number
j_2SeqNum	vikingSource	VIKINGv20170715	the running number of the J_2 detection	int	4		-99999999	meta.number
j_2SeqNum	vvvSource	VVVDR5	the running number of the J_2 detection	int	4		-99999999	meta.number;em.IR.J
j_2SeqNum	vvvxSource	VVVXDR1	the running number of the J_2 detection	int	4		-99999999	meta.id;em.IR.J
j_2SerMag2D	vikingSource	VIKINGv20151230	Extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_2SerMag2D	vikingSource	VIKINGv20160406	Extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_2SerMag2D	vikingSource	VIKINGv20161202	Extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_2SerMag2D	vikingSource	VIKINGv20170715	Extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
j_2SerMag2DErr	vikingSource	VIKINGv20151230	Error in extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2SerMag2DErr	vikingSource	VIKINGv20160406	Error in extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2SerMag2DErr	vikingSource	VIKINGv20161202	Error in extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2SerMag2DErr	vikingSource	VIKINGv20170715	Error in extended source J_2 mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag
j_2Xi	vikingSource	VIKINGv20151230	Offset of J_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi	vikingSource	VIKINGv20160406	Offset of J_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi	vikingSource	VIKINGv20161202	Offset of J_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi	vikingSource	VIKINGv20170715	Offset of J_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi	vvvSource	VVVDR5	Offset of J_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi	vvvxSource	VVVXDR1	Offset of J_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_5sig_ba	twomass_xsc	TWOMASS	J minor/major axis ratio fit to the 5-sigma isophote.	real	4			phys.size.axisRatio
j_5sig_phi	twomass_xsc	TWOMASS	J angle to 5-sigma major axis (E of N).	smallint	2	degrees		stat.error
j_5surf	twomass_xsc	TWOMASS	J central surface brightness (r<=5).	real	4	mag		phot.mag.sb
j_ba	twomass_xsc	TWOMASS	J minor/major axis ratio fit to the 3-sigma isophote.	real	4			phys.size.axisRatio
j_back	twomass_xsc	TWOMASS	J coadd median background.	real	4			meta.code
j_bisym_chi	twomass_xsc	TWOMASS	J bi-symmetric cross-correlation chi.	real	4			stat.fit.param
j_bisym_rat	twomass_xsc	TWOMASS	J bi-symmetric flux ratio.	real	4			phot.flux;arith.ratio
j_bndg_amp	twomass_xsc	TWOMASS	J banding maximum FT amplitude on this side of coadd.	real	4	DN		stat.fit.param
j_bndg_per	twomass_xsc	TWOMASS	J banding Fourier Transf. period on this side of coadd.	int	4	arcsec		stat.fit.param
j_chif_ellf	twomass_xsc	TWOMASS	J % chi-fraction for elliptical fit to 3-sig isophote.	real	4			stat.fit.param
j_cmsig	twomass_psc	TWOMASS	Corrected photometric uncertainty for the default J-band magnitude.	real	4	mag	J-band	phot.flux
j_con_indx	twomass_xsc	TWOMASS	J concentration index r_75%/r_25%.	real	4			phys.size;arith.ratio
j_d_area	twomass_xsc	TWOMASS	J 5-sigma to 3-sigma differential area.	smallint	2			stat.fit.residual
j_flg_10	twomass_xsc	TWOMASS	J confusion flag for 10 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_15	twomass_xsc	TWOMASS	J confusion flag for 15 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_20	twomass_xsc	TWOMASS	J confusion flag for 20 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_25	twomass_xsc	TWOMASS	J confusion flag for 25 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_30	twomass_xsc	TWOMASS	J confusion flag for 30 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_40	twomass_xsc	TWOMASS	J confusion flag for 40 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_5	twomass_xsc	TWOMASS	J confusion flag for 5 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_50	twomass_xsc	TWOMASS	J confusion flag for 50 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_60	twomass_xsc	TWOMASS	J confusion flag for 60 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_7	twomass_sixx2_xsc	TWOMASS	J confusion flag for 7 arcsec circular ap. mag	smallint	2
j_flg_7	twomass_xsc	TWOMASS	J confusion flag for 7 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_70	twomass_xsc	TWOMASS	J confusion flag for 70 arcsec circular ap. mag.	smallint	2			meta.code
j_flg_c	twomass_xsc	TWOMASS	J confusion flag for Kron circular mag.	smallint	2			meta.code
j_flg_e	twomass_xsc	TWOMASS	J confusion flag for Kron elliptical mag.	smallint	2			meta.code
j_flg_fc	twomass_xsc	TWOMASS	J confusion flag for fiducial Kron circ. mag.	smallint	2			meta.code
j_flg_fe	twomass_xsc	TWOMASS	J confusion flag for fiducial Kron ell. mag.	smallint	2			meta.code
j_flg_i20c	twomass_xsc	TWOMASS	J confusion flag for 20mag/sq." iso. circ. mag.	smallint	2			meta.code
j_flg_i20e	twomass_xsc	TWOMASS	J confusion flag for 20mag/sq." iso. ell. mag.	smallint	2			meta.code
j_flg_i21c	twomass_xsc	TWOMASS	J confusion flag for 21mag/sq." iso. circ. mag.	smallint	2			meta.code
j_flg_i21e	twomass_xsc	TWOMASS	J confusion flag for 21mag/sq." iso. ell. mag.	smallint	2			meta.code
j_flg_j21fc	twomass_xsc	TWOMASS	J confusion flag for 21mag/sq." iso. fid. circ. mag.	smallint	2			meta.code
j_flg_j21fe	twomass_xsc	TWOMASS	J confusion flag for 21mag/sq." iso. fid. ell. mag.	smallint	2			meta.code
j_flg_k20fc	twomass_xsc	TWOMASS	J confusion flag for 20mag/sq." iso. fid. circ. mag.	smallint	2			meta.code
j_flg_k20fe	twomass_sixx2_xsc	TWOMASS	J confusion flag for 20mag/sq.″ iso. fid. ell. mag	smallint	2
j_flg_k20fe	twomass_xsc	TWOMASS	J confusion flag for 20mag/sq." iso. fid. ell. mag.	smallint	2			meta.code
j_h	twomass_sixx2_psc	TWOMASS	The J-H color, computed from the J-band and H-band magnitudes (j_m and h_m, respectively) of the source. In cases where the first or second digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected.	real	4
j_k	twomass_sixx2_psc	TWOMASS	The J-Ks color, computed from the J-band and Ks-band magnitudes (j_m and k_m, respectively) of the source. In cases where the first or third digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected.	real	4
j_m	twomass_psc	TWOMASS	Default J-band magnitude	real	4	mag		phot.flux
j_m	twomass_sixx2_psc	TWOMASS	J selected "default" magnitude	real	4	mag
j_m_10	twomass_xsc	TWOMASS	J 10 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_15	twomass_xsc	TWOMASS	J 15 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_20	twomass_xsc	TWOMASS	J 20 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_25	twomass_xsc	TWOMASS	J 25 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_2mass	allwise_sc	WISE	2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is "null".	float	8	mag
j_m_2mass	wise_allskysc	WISE	2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source. This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is default.	real	4	mag	-0.9999995e9
j_m_2mass	wise_prelimsc	WISE	2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is default	real	4	mag	-0.9999995e9
j_m_30	twomass_xsc	TWOMASS	J 30 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_40	twomass_xsc	TWOMASS	J 40 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_5	twomass_xsc	TWOMASS	J 5 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_50	twomass_xsc	TWOMASS	J 50 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_60	twomass_xsc	TWOMASS	J 60 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_7	twomass_sixx2_xsc	TWOMASS	J 7 arcsec radius circular aperture magnitude	real	4	mag
j_m_7	twomass_xsc	TWOMASS	J 7 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_70	twomass_xsc	TWOMASS	J 70 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
j_m_c	twomass_xsc	TWOMASS	J Kron circular aperture magnitude.	real	4	mag		phot.flux
j_m_e	twomass_xsc	TWOMASS	J Kron elliptical aperture magnitude.	real	4	mag		phot.flux
j_m_ext	twomass_sixx2_xsc	TWOMASS	J mag from fit extrapolation	real	4	mag
j_m_ext	twomass_xsc	TWOMASS	J mag from fit extrapolation.	real	4	mag		phot.flux
j_m_fc	twomass_xsc	TWOMASS	J fiducial Kron circular magnitude.	real	4	mag		phot.flux
j_m_fe	twomass_xsc	TWOMASS	J fiducial Kron ell. mag aperture magnitude.	real	4	mag		phot.flux
j_m_i20c	twomass_xsc	TWOMASS	J 20mag/sq." isophotal circular ap. magnitude.	real	4	mag		phot.flux
j_m_i20e	twomass_xsc	TWOMASS	J 20mag/sq." isophotal elliptical ap. magnitude.	real	4	mag		phot.flux
j_m_i21c	twomass_xsc	TWOMASS	J 21mag/sq." isophotal circular ap. magnitude.	real	4	mag		phot.flux
j_m_i21e	twomass_xsc	TWOMASS	J 21mag/sq." isophotal elliptical ap. magnitude.	real	4	mag		phot.flux
j_m_j21fc	twomass_xsc	TWOMASS	J 21mag/sq." isophotal fiducial circ. ap. mag.	real	4	mag		phot.flux
j_m_j21fe	twomass_xsc	TWOMASS	J 21mag/sq." isophotal fiducial ell. ap. magnitude.	real	4	mag		phot.flux
j_m_k20fc	twomass_xsc	TWOMASS	J 20mag/sq." isophotal fiducial circ. ap. mag.	real	4	mag		phot.flux
J_M_K20FE	twomass	SIXDF	J 20mag/sq." isophotal fiducial ell. ap. magnitude	real	4	mag
j_m_k20fe	twomass_sixx2_xsc	TWOMASS	J 20mag/sq.″ isophotal fiducial ell. ap. magnitude	real	4	mag
j_m_k20fe	twomass_xsc	TWOMASS	J 20mag/sq." isophotal fiducial ell. ap. magnitude.	real	4	mag		phot.flux
j_m_stdap	twomass_psc	TWOMASS	J-band "standard" aperture magnitude.	real	4	mag		phot.flux
j_m_sys	twomass_xsc	TWOMASS	J system photometry magnitude.	real	4	mag		phot.flux
j_mnsurfb_eff	twomass_xsc	TWOMASS	J mean surface brightness at the half-light radius.	real	4	mag		phot.mag.sb
j_msig	twomass_sixx2_psc	TWOMASS	J "default" mag uncertainty	real	4	mag
j_msig_10	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 10 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_15	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 15 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_20	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 20 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_25	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 25 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_2mass	allwise_sc	WISE	2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is "null".	float	8	mag
j_msig_2mass	wise_allskysc	WISE	2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is default.	real	4	mag	-0.9999995e9
j_msig_2mass	wise_prelimsc	WISE	2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is default	real	4	mag	-0.9999995e9
j_msig_30	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 30 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_40	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 40 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_5	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 5 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_50	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 50 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_60	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 60 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_7	twomass_sixx2_xsc	TWOMASS	J 1-sigma uncertainty in 7 arcsec circular ap. mag	real	4	mag
j_msig_7	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 7 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_70	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 70 arcsec circular ap. mag.	real	4	mag		stat.error
j_msig_c	twomass_xsc	TWOMASS	J 1-sigma uncertainty in Kron circular mag.	real	4	mag		stat.error
j_msig_e	twomass_xsc	TWOMASS	J 1-sigma uncertainty in Kron elliptical mag.	real	4	mag		stat.error
j_msig_ext	twomass_sixx2_xsc	TWOMASS	J 1-sigma uncertainty in mag from fit extrapolation	real	4	mag
j_msig_ext	twomass_xsc	TWOMASS	J 1-sigma uncertainty in mag from fit extrapolation.	real	4	mag		stat.error
j_msig_fc	twomass_xsc	TWOMASS	J 1-sigma uncertainty in fiducial Kron circ. mag.	real	4	mag		stat.error
j_msig_fe	twomass_xsc	TWOMASS	J 1-sigma uncertainty in fiducial Kron ell. mag.	real	4	mag		stat.error
j_msig_i20c	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 20mag/sq." iso. circ. mag.	real	4	mag		stat.error
j_msig_i20e	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 20mag/sq." iso. ell. mag.	real	4	mag		stat.error
j_msig_i21c	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 21mag/sq." iso. circ. mag.	real	4	mag		stat.error
j_msig_i21e	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 21mag/sq." iso. ell. mag.	real	4	mag		stat.error
j_msig_j21fc	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 21mag/sq." iso.fid.circ.mag.	real	4	mag		stat.error
j_msig_j21fe	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 21mag/sq." iso.fid.ell.mag.	real	4	mag		stat.error
j_msig_k20fc	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 20mag/sq." iso.fid.circ. mag.	real	4	mag		stat.error
j_msig_k20fe	twomass_xsc	TWOMASS	J 1-sigma uncertainty in 20mag/sq." iso.fid.ell.mag.	real	4	mag		stat.error
j_msig_stdap	twomass_psc	TWOMASS	Uncertainty in the J-band standard aperture magnitude.	real	4	mag		phot.flux
j_msig_sys	twomass_xsc	TWOMASS	J 1-sigma uncertainty in system photometry mag.	real	4	mag		stat.error
j_msigcom	twomass_psc	TWOMASS	Combined, or total photometric uncertainty for the default J-band magnitude.	real	4	mag	J-band	phot.flux
j_msigcom	twomass_sixx2_psc	TWOMASS	combined (total) J band photometric uncertainty	real	4	mag
j_msnr10	twomass_scn	TWOMASS	The estimated J-band magnitude at which SNR=10 is achieved for this scan.	real	4	mag		phot.flux
j_msnr10	twomass_sixx2_scn	TWOMASS	J mag at which SNR=10 is achieved, from j_psp and j_zp_ap	real	4	mag
j_n_snr10	twomass_scn	TWOMASS	Number of point sources at J-band with SNR>10 (instrumental mag <=15.8)	int	4			meta.number
j_n_snr10	twomass_sixx2_scn	TWOMASS	number of J point sources with SNR>10 (instrumental m<=15.8)	int	4
j_pchi	twomass_xsc	TWOMASS	J chi^2 of fit to rad. profile (LCSB: alpha scale len).	real	4			stat.fit.param
j_peak	twomass_xsc	TWOMASS	J peak pixel brightness.	real	4	mag		phot.mag.sb
j_perc_darea	twomass_xsc	TWOMASS	J 5-sigma to 3-sigma percent area change.	smallint	2			FIT_PARAM
j_phi	twomass_xsc	TWOMASS	J angle to 3-sigma major axis (E of N).	smallint	2	degrees		pos.posAng
j_psfchi	twomass_psc	TWOMASS	Reduced chi-squared goodness-of-fit value for the J-band profile-fit photometry made on the 1.3 s "Read_2" exposures.	real	4			stat.fit.param
j_psp	twomass_scn	TWOMASS	J-band photometric sensitivity paramater (PSP).	real	4			instr.sensitivity
j_psp	twomass_sixx2_scn	TWOMASS	J photometric sensitivity param: j_shape_avg*(j_fbg_avg^.29)	real	4
j_pts_noise	twomass_scn	TWOMASS	Base-10 logarithm of the mode of the noise distribution for all point source detections in the scan, where the noise is estimated from the measured J-band photometric errors and is expressed in units of mJy.	real	4			instr.det.noise
j_pts_noise	twomass_sixx2_scn	TWOMASS	log10 of J band modal point src noise estimate	real	4	logmJy
j_r_c	twomass_xsc	TWOMASS	J Kron circular aperture radius.	real	4	arcsec		phys.angSize;src
j_r_e	twomass_xsc	TWOMASS	J Kron elliptical aperture semi-major axis.	real	4	arcsec		phys.angSize;src
j_r_eff	twomass_xsc	TWOMASS	J half-light (integrated half-flux point) radius.	real	4	arcsec		phys.angSize;src
j_r_i20c	twomass_xsc	TWOMASS	J 20mag/sq." isophotal circular aperture radius.	real	4	arcsec		phys.angSize;src
j_r_i20e	twomass_xsc	TWOMASS	J 20mag/sq." isophotal elliptical ap. semi-major axis.	real	4	arcsec		phys.angSize;src
j_r_i21c	twomass_xsc	TWOMASS	J 21mag/sq." isophotal circular aperture radius.	real	4	arcsec		phys.angSize;src
j_r_i21e	twomass_xsc	TWOMASS	J 21mag/sq." isophotal elliptical ap. semi-major axis.	real	4	arcsec		phys.angSize;src
j_resid_ann	twomass_xsc	TWOMASS	J residual annulus background median.	real	4	DN		meta.code
j_sc_1mm	twomass_xsc	TWOMASS	J 1st moment (score) (LCSB: super blk 2,4,8 SNR).	real	4			meta.code
j_sc_2mm	twomass_xsc	TWOMASS	J 2nd moment (score) (LCSB: SNRMAX - super SNR max).	real	4			meta.code
j_sc_msh	twomass_xsc	TWOMASS	J median shape score.	real	4			meta.code
j_sc_mxdn	twomass_xsc	TWOMASS	J mxdn (score) (LCSB: BSNR - block/smoothed SNR).	real	4			meta.code
j_sc_r1	twomass_xsc	TWOMASS	J r1 (score).	real	4			meta.code
j_sc_r23	twomass_xsc	TWOMASS	J r23 (score) (LCSB: TSNR - integrated SNR for r=15).	real	4			meta.code
j_sc_sh	twomass_xsc	TWOMASS	J shape (score).	real	4			meta.code
j_sc_vint	twomass_xsc	TWOMASS	J vint (score).	real	4			meta.code
j_sc_wsh	twomass_xsc	TWOMASS	J wsh (score) (LCSB: PSNR - peak raw SNR).	real	4			meta.code
j_seetrack	twomass_xsc	TWOMASS	J band seetracking score.	real	4			meta.code
j_sh0	twomass_xsc	TWOMASS	J ridge shape (LCSB: BSNR limit).	real	4			FIT_PARAM
j_shape_avg	twomass_scn	TWOMASS	J-band average seeing shape for scan.	real	4			instr.obsty.seeing
j_shape_avg	twomass_sixx2_scn	TWOMASS	J band average seeing shape for scan	real	4
j_shape_rms	twomass_scn	TWOMASS	RMS-error of J-band average seeing shape.	real	4			instr.obsty.seeing
j_shape_rms	twomass_sixx2_scn	TWOMASS	rms of J band avg seeing shape for scan	real	4
j_sig_sh0	twomass_xsc	TWOMASS	J ridge shape sigma (LCSB: B2SNR limit).	real	4			FIT_PARAM
j_snr	twomass_psc	TWOMASS	J-band "scan" signal-to-noise ratio.	real	4	mag		instr.det.noise
j_snr	twomass_sixx2_psc	TWOMASS	J band "scan" signal-to-noise ratio	real	4
j_subst2	twomass_xsc	TWOMASS	J residual background #2 (score).	real	4			meta.code
j_zp_ap	twomass_scn	TWOMASS	Photometric zero-point for J-band aperture photometry.	real	4	mag		phot.mag;arith.zp
j_zp_ap	twomass_sixx2_scn	TWOMASS	J band ap. calibration photometric zero-point for scan	real	4	mag
jAmpl	vmcCepheidVariables	VMCDR4	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20160311	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20160822	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20170109	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20170411	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20171101	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20180702	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20181120	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20191212	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20210708	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20230816	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmpl	vmcCepheidVariables	VMCv20240226	Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCDR4	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20160311	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20160822	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20170109	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20170411	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20171101	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20180702	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20181120	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20191212	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20210708	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20230816	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAmplErr	vmcCepheidVariables	VMCv20240226	Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.J
jAperJky3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
jAperJky3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
jAperJky3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
jAperJky3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJky4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJky4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJky4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJky6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJky6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJky6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJky6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
jAperJkyNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperJkyNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
jAperLup3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
jAperLup3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
jAperLup3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
jAperLup3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLup4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLup4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLup4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source J (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source J (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source J (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLup6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLup6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLup6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source J (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source J (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLup6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source J (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
jAperLupNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperLupNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
jAperMag1	vmcSynopticSource	VMCDR1	Extended source J aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vmcSynopticSource	VMCDR2	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCDR3	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCDR4	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCDR5	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20110816	Extended source J aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vmcSynopticSource	VMCv20110909	Extended source J aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vmcSynopticSource	VMCv20120126	Extended source J aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vmcSynopticSource	VMCv20121128	Extended source J aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vmcSynopticSource	VMCv20130304	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vmcSynopticSource	VMCv20130805	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20140428	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20140903	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20150309	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20151218	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20160311	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20160822	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20170109	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20170411	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20171101	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20180702	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20181120	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20191212	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20210708	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20230816	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcSynopticSource	VMCv20240226	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vvvSource	VVVDR1	Extended source J aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vvvSource	VVVDR5	Point source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vvvSource	VVVv20100531	Extended source J aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vvvSource	VVVv20110718	Extended source J aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag1	vvvSource, vvvSynopticSource	VVVDR2	Extended source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1	vvvxSource	VVVXDR1	Point source J aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCDR1	Error in extended source J mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCDR2	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCDR3	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag1Err	vmcSynopticSource	VMCDR4	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCDR5	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20110816	Error in extended source J mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCv20110909	Error in extended source J mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCv20120126	Error in extended source J mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCv20121128	Error in extended source J mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCv20130304	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCv20130805	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vmcSynopticSource	VMCv20140428	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20140903	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag1Err	vmcSynopticSource	VMCv20150309	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag1Err	vmcSynopticSource	VMCv20151218	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20160311	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20160822	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20170109	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20170411	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20171101	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20180702	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20181120	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20191212	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20210708	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20230816	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcSynopticSource	VMCv20240226	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vvvSource	VVVDR1	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vvvSource	VVVDR5	Error in point source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag1Err	vvvSource	VVVv20100531	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vvvSource	VVVv20110718	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vvvSource, vvvSynopticSource	VVVDR2	Error in extended source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag1Err	vvvxSource	VVVXDR1	Error in point source J mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCDR1	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vmcSynopticSource	VMCDR2	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCDR3	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCDR4	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCDR5	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20110816	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vmcSynopticSource	VMCv20110909	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vmcSynopticSource	VMCv20120126	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vmcSynopticSource	VMCv20121128	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vmcSynopticSource	VMCv20130304	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vmcSynopticSource	VMCv20130805	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20140428	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20140903	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20150309	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20151218	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20160311	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20160822	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20170109	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20170411	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20171101	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20180702	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20181120	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20191212	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20210708	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20230816	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcSynopticSource	VMCv20240226	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2	vvvSynopticSource	VVVDR1	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag2	vvvSynopticSource	VVVDR2	Extended source J aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCDR1	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCDR2	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCDR3	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag2Err	vmcSynopticSource	VMCDR4	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCDR5	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20110816	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCv20110909	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCv20120126	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCv20121128	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCv20130304	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCv20130805	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vmcSynopticSource	VMCv20140428	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20140903	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag2Err	vmcSynopticSource	VMCv20150309	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag2Err	vmcSynopticSource	VMCv20151218	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20160311	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20160822	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20170109	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20170411	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20171101	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20180702	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20181120	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20191212	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20210708	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20230816	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcSynopticSource	VMCv20240226	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag2Err	vvvSynopticSource	VVVDR1	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag2Err	vvvSynopticSource	VVVDR2	Error in extended source J mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3	ultravistaSource	ULTRAVISTADR4	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source J aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vhsSource	VHSDR1	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vhsSource	VHSDR2	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vhsSource	VHSDR3	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSDR4	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSDR5	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSDR6	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20120926	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vhsSource	VHSv20130417	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vhsSource	VHSv20140409	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20150108	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20160114	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20160507	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20170630	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20180419	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20201209	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20231101	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vhsSource	VHSv20240731	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	videoSource	VIDEODR2	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	videoSource	VIDEODR3	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	videoSource	VIDEODR4	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	videoSource	VIDEODR5	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	videoSource	VIDEOv20100513	Default point/extended source J mag, no aperture correction applied If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	videoSource	VIDEOv20111208	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingSource	VIKINGDR2	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingSource	VIKINGDR3	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingSource	VIKINGDR4	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingSource	VIKINGv20110714	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingSource	VIKINGv20111019	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingSource	VIKINGv20130417	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingSource	VIKINGv20140402	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingSource	VIKINGv20150421	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingSource	VIKINGv20151230	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingSource	VIKINGv20160406	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingSource	VIKINGv20161202	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingSource	VIKINGv20170715	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source J aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source J aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCDR1	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCDR2	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCDR3	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCDR4	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCDR5	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20110816	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCv20110909	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCv20120126	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCv20121128	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCv20130304	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSource	VMCv20130805	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20140428	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20140903	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20150309	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20151218	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20160311	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20160822	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20170109	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20170411	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20171101	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20180702	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20181120	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20191212	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20210708	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20230816	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSource	VMCv20240226	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCDR1	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSynopticSource	VMCDR2	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCDR3	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCDR4	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCDR5	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20110816	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSynopticSource	VMCv20110909	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSynopticSource	VMCv20120126	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSynopticSource	VMCv20121128	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSynopticSource	VMCv20130304	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vmcSynopticSource	VMCv20130805	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20140428	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20140903	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20150309	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20151218	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20160311	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20160822	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20170109	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20170411	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20171101	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20180702	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20181120	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20191212	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20210708	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20230816	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcSynopticSource	VMCv20240226	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcdeepSource	VMCDEEPv20230713	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcdeepSource	VMCDEEPv20240506	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcdeepSynopticSource	VMCDEEPv20230713	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vmcdeepSynopticSource	VMCDEEPv20240506	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vvvSource	VVVDR1	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vvvSource	VVVDR2	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vvvSource	VVVDR5	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vvvSource	VVVv20100531	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vvvSource	VVVv20110718	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vvvSynopticSource	VVVDR1	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag3	vvvSynopticSource	VVVDR2	Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3	vvvVivaCatalogue	VVVDR5	J magnitude using aperture corrected mag (2.0 arcsec aperture diameter, from VVVDR4 1st epoch JHKs contemporaneous OB) {catalogue TType keyword: jAperMag3}	real	4	mag	-9.999995e8
jAperMag3	vvvxSource	VVVXDR1	Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag3Err	ultravistaSource	ULTRAVISTADR4	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vhsSource	VHSDR1	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vhsSource	VHSDR2	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vhsSource	VHSDR3	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag3Err	vhsSource	VHSDR4	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	vhsSource	VHSDR5	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSDR6	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20120926	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vhsSource	VHSv20130417	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vhsSource	VHSv20140409	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag3Err	vhsSource	VHSv20150108	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	vhsSource	VHSv20160114	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20160507	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20170630	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20180419	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20201209	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20231101	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vhsSource	VHSv20240731	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	videoSource	VIDEODR2	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	videoSource	VIDEODR3	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	videoSource	VIDEODR4	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	videoSource	VIDEODR5	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	videoSource	VIDEOv20100513	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	videoSource	VIDEOv20111208	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGDR2	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGDR3	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGDR4	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag3Err	vikingSource	VIKINGv20110714	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGv20111019	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGv20130417	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGv20140402	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingSource	VIKINGv20150421	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	vikingSource	VIKINGv20151230	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vikingSource	VIKINGv20160406	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vikingSource	VIKINGv20161202	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vikingSource	VIKINGv20170715	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCDR2	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCDR3	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	vmcSource	VMCDR4	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCDR5	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20110816	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCv20110909	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCv20120126	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCv20121128	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCv20130304	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCv20130805	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcSource	VMCv20140428	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag3Err	vmcSource	VMCv20140903	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	vmcSource	VMCv20150309	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag3Err	vmcSource	VMCv20151218	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20160311	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20160822	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20170109	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20170411	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20171101	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20180702	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20181120	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20191212	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20210708	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20230816	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource	VMCv20240226	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcSource, vmcSynopticSource	VMCDR1	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vmcdeepSource	VMCDEEPv20240506	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vvvSource	VVVDR2	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vvvSource	VVVDR5	Error in default point source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag3Err	vvvSource	VVVv20100531	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vvvSource	VVVv20110718	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vvvSource, vvvSynopticSource	VVVDR1	Error in default point/extended source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag3Err	vvvVivaCatalogue	VVVDR5	Error in default point source J mag, from VVVDR4 {catalogue TType keyword: jAperMag3Err}	real	4	mag	-9.999995e8
jAperMag3Err	vvvxSource	VVVXDR1	Error in default point source J mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4	ultravistaSource	ULTRAVISTADR4	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source J aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vhsSource	VHSDR1	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vhsSource	VHSDR2	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vhsSource	VHSDR3	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSDR4	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSDR5	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSDR6	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20120926	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vhsSource	VHSv20130417	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vhsSource	VHSv20140409	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20150108	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20160114	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20160507	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20170630	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20180419	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20201209	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20231101	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vhsSource	VHSv20240731	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	videoSource	VIDEODR2	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	videoSource	VIDEODR3	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	videoSource	VIDEODR4	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	videoSource	VIDEODR5	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	videoSource	VIDEOv20100513	Extended source J mag, no aperture correction applied	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	videoSource	VIDEOv20111208	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingSource	VIKINGDR2	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingSource	VIKINGDR3	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingSource	VIKINGDR4	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingSource	VIKINGv20110714	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingSource	VIKINGv20111019	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingSource	VIKINGv20130417	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingSource	VIKINGv20140402	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingSource	VIKINGv20150421	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingSource	VIKINGv20151230	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingSource	VIKINGv20160406	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingSource	VIKINGv20161202	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingSource	VIKINGv20170715	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source J aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source J aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCDR1	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCDR2	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCDR3	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCDR4	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCDR5	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20110816	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCv20110909	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCv20120126	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCv20121128	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCv20130304	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSource	VMCv20130805	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20140428	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20140903	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20150309	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20151218	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20160311	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20160822	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20170109	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20170411	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20171101	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20180702	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20181120	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20191212	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20210708	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20230816	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSource	VMCv20240226	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCDR1	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSynopticSource	VMCDR2	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCDR3	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCDR4	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCDR5	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20110816	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSynopticSource	VMCv20110909	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSynopticSource	VMCv20120126	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSynopticSource	VMCv20121128	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSynopticSource	VMCv20130304	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vmcSynopticSource	VMCv20130805	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20140428	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20140903	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20150309	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20151218	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20160311	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20160822	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20170109	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20170411	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20171101	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20180702	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20181120	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20191212	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20210708	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20230816	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcSynopticSource	VMCv20240226	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcdeepSource	VMCDEEPv20230713	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcdeepSource	VMCDEEPv20240506	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vvvSource	VVVDR2	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vvvSource	VVVDR5	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4	vvvSource	VVVv20100531	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vvvSource	VVVv20110718	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vvvSource, vvvSynopticSource	VVVDR1	Extended source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag4	vvvxSource	VVVXDR1	Point source J aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag4Err	ultravistaSource	ULTRAVISTADR4	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source J (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vhsSource	VHSDR1	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vhsSource	VHSDR2	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vhsSource	VHSDR3	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag4Err	vhsSource	VHSDR4	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vhsSource	VHSDR5	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSDR6	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20120926	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vhsSource	VHSv20130417	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vhsSource	VHSv20140409	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag4Err	vhsSource	VHSv20150108	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vhsSource	VHSv20160114	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20160507	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20170630	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20180419	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20201209	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20231101	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vhsSource	VHSv20240731	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	videoSource	VIDEODR2	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	videoSource	VIDEODR3	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	videoSource	VIDEODR4	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	videoSource	VIDEODR5	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	videoSource	VIDEOv20100513	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	videoSource	VIDEOv20111208	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGDR2	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGDR3	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGDR4	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag4Err	vikingSource	VIKINGv20110714	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGv20111019	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGv20130417	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGv20140402	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingSource	VIKINGv20150421	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vikingSource	VIKINGv20151230	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vikingSource	VIKINGv20160406	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vikingSource	VIKINGv20161202	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vikingSource	VIKINGv20170715	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source J (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source J (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCDR1	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCDR2	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCDR3	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vmcSource	VMCDR4	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCDR5	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20110816	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCv20110909	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCv20120126	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCv20121128	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCv20130304	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCv20130805	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSource	VMCv20140428	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag4Err	vmcSource	VMCv20140903	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vmcSource	VMCv20150309	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vmcSource	VMCv20151218	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20160311	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20160822	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20170109	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20170411	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20171101	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20180702	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20181120	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20191212	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20210708	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20230816	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSource	VMCv20240226	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCDR1	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCDR2	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCDR3	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vmcSynopticSource	VMCDR4	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCDR5	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20110816	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCv20110909	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCv20120126	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCv20121128	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCv20130304	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCv20130805	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vmcSynopticSource	VMCv20140428	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20140903	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vmcSynopticSource	VMCv20150309	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag4Err	vmcSynopticSource	VMCv20151218	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20160311	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20160822	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20170109	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20170411	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20171101	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20180702	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20181120	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20191212	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20210708	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20230816	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcSynopticSource	VMCv20240226	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcdeepSource	VMCDEEPv20230713	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcdeepSource	VMCDEEPv20240506	Error in point/extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vvvSource	VVVDR2	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vvvSource	VVVDR5	Error in point source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag4Err	vvvSource	VVVv20100531	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vvvSource	VVVv20110718	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vvvSource, vvvSynopticSource	VVVDR1	Error in extended source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag4Err	vvvxSource	VVVXDR1	Error in point source J mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCDR1	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vmcSynopticSource	VMCDR2	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCDR3	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCDR4	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCDR5	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20110816	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vmcSynopticSource	VMCv20110909	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vmcSynopticSource	VMCv20120126	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vmcSynopticSource	VMCv20121128	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vmcSynopticSource	VMCv20130304	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vmcSynopticSource	VMCv20130805	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20140428	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20140903	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20150309	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20151218	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20160311	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20160822	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20170109	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20170411	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20171101	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20180702	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20181120	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20191212	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20210708	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20230816	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcSynopticSource	VMCv20240226	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5	vvvSynopticSource	VVVDR1	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag5	vvvSynopticSource	VVVDR2	Extended source J aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCDR1	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCDR2	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCDR3	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag5Err	vmcSynopticSource	VMCDR4	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCDR5	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20110816	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCv20110909	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCv20120126	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCv20121128	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCv20130304	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCv20130805	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vmcSynopticSource	VMCv20140428	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20140903	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag5Err	vmcSynopticSource	VMCv20150309	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag5Err	vmcSynopticSource	VMCv20151218	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20160311	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20160822	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20170109	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20170411	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20171101	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20180702	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20181120	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20191212	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20210708	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20230816	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcSynopticSource	VMCv20240226	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag5Err	vvvSynopticSource	VVVDR1	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag5Err	vvvSynopticSource	VVVDR2	Error in extended source J mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6	ultravistaSource	ULTRAVISTADR4	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source J aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vhsSource	VHSDR1	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vhsSource	VHSDR2	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vhsSource	VHSDR3	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSDR4	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSDR5	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSDR6	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20120926	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vhsSource	VHSv20130417	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vhsSource	VHSv20140409	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20150108	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20160114	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20160507	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20170630	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20180419	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20201209	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20231101	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vhsSource	VHSv20240731	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	videoSource	VIDEODR2	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	videoSource	VIDEODR3	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	videoSource	VIDEODR4	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	videoSource	VIDEODR5	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	videoSource	VIDEOv20100513	Extended source J mag, no aperture correction applied	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	videoSource	VIDEOv20111208	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingSource	VIKINGDR2	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingSource	VIKINGDR3	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingSource	VIKINGDR4	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingSource	VIKINGv20110714	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingSource	VIKINGv20111019	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingSource	VIKINGv20130417	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingSource	VIKINGv20140402	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingSource	VIKINGv20150421	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingSource	VIKINGv20151230	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingSource	VIKINGv20160406	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingSource	VIKINGv20161202	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingSource	VIKINGv20170715	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source J aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source J aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCDR1	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCDR2	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCDR3	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCDR4	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCDR5	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20110816	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCv20110909	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCv20120126	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCv20121128	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCv20130304	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMag6	vmcSource	VMCv20130805	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20140428	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20140903	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20150309	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20151218	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20160311	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20160822	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20170109	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20170411	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20171101	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20180702	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20181120	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20191212	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20210708	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20230816	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcSource	VMCv20240226	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcdeepSource	VMCDEEPv20230713	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6	vmcdeepSource	VMCDEEPv20240506	Point source J aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMag6Err	ultravistaSource	ULTRAVISTADR4	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source J (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vhsSource	VHSDR1	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vhsSource	VHSDR2	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vhsSource	VHSDR3	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag6Err	vhsSource	VHSDR4	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	vhsSource	VHSDR5	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSDR6	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20120926	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vhsSource	VHSv20130417	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vhsSource	VHSv20140409	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag6Err	vhsSource	VHSv20150108	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	vhsSource	VHSv20160114	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20160507	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20170630	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20180419	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20201209	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20231101	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vhsSource	VHSv20240731	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	videoSource	VIDEODR2	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	videoSource	VIDEODR3	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	videoSource	VIDEODR4	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	videoSource	VIDEODR5	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	videoSource	VIDEOv20100513	Error in extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	videoSource	VIDEOv20111208	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGDR2	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGDR3	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGDR4	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag6Err	vikingSource	VIKINGv20110714	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGv20111019	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGv20130417	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGv20140402	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingSource	VIKINGv20150421	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	vikingSource	VIKINGv20151230	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vikingSource	VIKINGv20160406	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vikingSource	VIKINGv20161202	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vikingSource	VIKINGv20170715	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source J (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source J (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCDR1	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCDR2	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCDR3	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	vmcSource	VMCDR4	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCDR5	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20110816	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCv20110909	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCv20120126	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCv20121128	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCv20130304	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCv20130805	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
jAperMag6Err	vmcSource	VMCv20140428	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jAperMag6Err	vmcSource	VMCv20140903	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	vmcSource	VMCv20150309	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jAperMag6Err	vmcSource	VMCv20151218	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20160311	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20160822	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20170109	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20170411	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20171101	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20180702	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20181120	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20191212	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20210708	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20230816	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcSource	VMCv20240226	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcdeepSource	VMCDEEPv20230713	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMag6Err	vmcdeepSource	VMCDEEPv20240506	Error in point/extended source J mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jAperMagNoAperCorr3	ultravistaSource	ULTRAVISTADR4	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vhsSource	VHSDR1	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vhsSource	VHSDR2	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vhsSource	VHSDR3	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSDR4	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSDR5	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSDR6	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20120926	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vhsSource	VHSv20130417	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vhsSource	VHSv20140409	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20150108	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20160114	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20160507	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20170630	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20180419	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20201209	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20231101	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vhsSource	VHSv20240731	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	videoSource	VIDEODR2	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	videoSource	VIDEODR3	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	videoSource	VIDEODR4	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	videoSource	VIDEODR5	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	videoSource	VIDEOv20111208	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingSource	VIKINGDR2	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingSource	VIKINGDR3	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingSource	VIKINGDR4	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingSource	VIKINGv20110714	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingSource	VIKINGv20111019	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingSource	VIKINGv20130417	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingSource	VIKINGv20140402	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingSource	VIKINGv20150421	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingSource	VIKINGv20151230	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingSource	VIKINGv20160406	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingSource	VIKINGv20161202	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingSource	VIKINGv20170715	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCDR1	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCDR2	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCDR3	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCDR4	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCDR5	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20110816	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCv20110909	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCv20120126	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCv20121128	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCv20130304	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr3	vmcSource	VMCv20130805	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20140428	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20140903	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20150309	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20151218	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20160311	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20160822	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20170109	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20170411	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20171101	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20180702	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20181120	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20191212	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20210708	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20230816	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcSource	VMCv20240226	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcdeepSource	VMCDEEPv20230713	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr3	vmcdeepSource	VMCDEEPv20240506	Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	ultravistaSource	ULTRAVISTADR4	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vhsSource	VHSDR1	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vhsSource	VHSDR2	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vhsSource	VHSDR3	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSDR4	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSDR5	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSDR6	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20120926	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vhsSource	VHSv20130417	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vhsSource	VHSv20140409	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20150108	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20160114	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20160507	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20170630	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20180419	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20201209	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20231101	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vhsSource	VHSv20240731	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	videoSource	VIDEODR2	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	videoSource	VIDEODR3	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	videoSource	VIDEODR4	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	videoSource	VIDEODR5	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	videoSource	VIDEOv20111208	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingSource	VIKINGDR2	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingSource	VIKINGDR3	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingSource	VIKINGDR4	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingSource	VIKINGv20110714	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingSource	VIKINGv20111019	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingSource	VIKINGv20130417	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingSource	VIKINGv20140402	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingSource	VIKINGv20150421	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingSource	VIKINGv20151230	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingSource	VIKINGv20160406	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingSource	VIKINGv20161202	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingSource	VIKINGv20170715	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCDR1	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCDR2	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCDR3	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCDR4	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCDR5	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20110816	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCv20110909	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCv20120126	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCv20121128	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCv20130304	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr4	vmcSource	VMCv20130805	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20140428	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20140903	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20150309	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20151218	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20160311	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20160822	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20170109	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20170411	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20171101	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20180702	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20181120	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20191212	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20210708	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20230816	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcSource	VMCv20240226	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcdeepSource	VMCDEEPv20230713	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr4	vmcdeepSource	VMCDEEPv20240506	Extended source J aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	ultravistaSource	ULTRAVISTADR4	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vhsSource	VHSDR1	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vhsSource	VHSDR2	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vhsSource	VHSDR3	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSDR4	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSDR5	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSDR6	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20120926	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vhsSource	VHSv20130417	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vhsSource	VHSv20140409	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20150108	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20160114	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20160507	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20170630	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20180419	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20201209	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20231101	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vhsSource	VHSv20240731	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	videoSource	VIDEODR2	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	videoSource	VIDEODR3	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	videoSource	VIDEODR4	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	videoSource	VIDEODR5	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	videoSource	VIDEOv20111208	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingSource	VIKINGDR2	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingSource	VIKINGDR3	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingSource	VIKINGDR4	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingSource	VIKINGv20110714	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingSource	VIKINGv20111019	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingSource	VIKINGv20130417	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingSource	VIKINGv20140402	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingSource	VIKINGv20150421	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingSource	VIKINGv20151230	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingSource	VIKINGv20160406	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingSource	VIKINGv20161202	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingSource	VIKINGv20170715	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCDR1	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCDR2	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCDR3	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCDR4	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCDR5	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20110816	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCv20110909	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCv20120126	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCv20121128	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCv20130304	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
jAperMagNoAperCorr6	vmcSource	VMCv20130805	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20140428	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20140903	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20150309	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20151218	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20160311	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20160822	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20170109	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20170411	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20171101	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20180702	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20181120	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20191212	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20210708	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20230816	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcSource	VMCv20240226	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcdeepSource	VMCDEEPv20230713	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jAperMagNoAperCorr6	vmcdeepSource	VMCDEEPv20240506	Extended source J aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jaStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGDR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGDR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20130417	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20140402	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20150421	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20151230	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20160406	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20161202	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vikingVarFrameSetInfo	VIKINGv20170715	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGDR3	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGDR4	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20130417	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20140402	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20150421	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20151230	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20160406	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20161202	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vikingVarFrameSetInfo	VIKINGv20170715	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jAverageConf	ultravistaSourceRemeasurement	ULTRAVISTADR4	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vhsSource	VHSDR1	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vhsSource	VHSDR2	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vhsSource	VHSDR3	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSDR4	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSDR5	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSDR6	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20120926	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	stat.likelihood;em.IR.NIR
jAverageConf	vhsSource	VHSv20130417	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vhsSource	VHSv20140409	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20150108	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20160114	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20160507	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20170630	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20180419	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20201209	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20231101	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vhsSource	VHSv20240731	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingSource	VIKINGDR2	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vikingSource	VIKINGDR3	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	stat.likelihood;em.IR.NIR
jAverageConf	vikingSource	VIKINGDR4	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingSource	VIKINGv20110714	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vikingSource	VIKINGv20111019	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vikingSource	VIKINGv20130417	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vikingSource	VIKINGv20140402	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vikingSource	VIKINGv20150421	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingSource	VIKINGv20151230	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingSource	VIKINGv20160406	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingSource	VIKINGv20161202	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingSource	VIKINGv20170715	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vmcSource	VMCDR2	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vmcSource	VMCDR3	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCDR4	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCDR5	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20110816	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vmcSource	VMCv20110909	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vmcSource	VMCv20120126	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vmcSource	VMCv20121128	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	stat.likelihood;em.IR.NIR
jAverageConf	vmcSource	VMCv20130304	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vmcSource	VMCv20130805	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vmcSource	VMCv20140428	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20140903	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20150309	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20151218	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20160311	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20160822	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20170109	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20170411	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20171101	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20180702	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20181120	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20191212	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20210708	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20230816	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource	VMCv20240226	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcSource, vmcSynopticSource	VMCDR1	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	meta.code
jAverageConf	vmcdeepSource	VMCDEEPv20240506	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vvvSource	VVVDR2	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
jAverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jAverageConf	vvvSource, vvvSynopticSource	VVVDR1	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-99999999	stat.likelihood;em.IR.NIR
jAverageConf	vvvxSource	VVVXDR1	average confidence in 2 arcsec diameter default aperture (aper3) J	real	4		-0.9999995e9	stat.likelihood;em.IR.J
jbestAper	ultravistaMapLcVariability	ULTRAVISTADR4	Best aperture (1-3) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	ultravistaVariability	ULTRAVISTADR4	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	videoVariability	VIDEODR2	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	videoVariability	VIDEODR3	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	videoVariability	VIDEODR4	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	videoVariability	VIDEODR5	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	videoVariability	VIDEOv20100513	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	videoVariability	VIDEOv20111208	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGDR2	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGDR3	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGDR4	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20110714	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20111019	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20130417	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20140402	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20150421	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20151230	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20160406	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20161202	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vikingVariability	VIKINGv20170715	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCDR1	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCDR2	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCDR3	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCDR4	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCDR5	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20110816	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20110909	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20120126	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20121128	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20130304	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20130805	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.NIR
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20140428	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20140903	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20150309	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20151218	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20160311	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20160822	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20170109	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20170411	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20171101	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20180702	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20181120	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20191212	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20210708	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20230816	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcVariability	VMCv20240226	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcdeepVariability	VMCDEEPv20230713	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vmcdeepVariability	VMCDEEPv20240506	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vvvVariability	VVVDR5	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vvvVariability	VVVv20100531	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper	vvvxVariability	VVVXDR1	Best aperture (1-6) for photometric statistics in the J band	int	4		-9999	meta.code.class;em.IR.J
			Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGDR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGDR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20130417	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20140402	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20150421	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20151230	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20160406	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20161202	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vikingVarFrameSetInfo	VIKINGv20170715	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGDR3	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGDR4	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20130417	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20140402	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20150421	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20151230	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20160406	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20161202	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vikingVarFrameSetInfo	VIKINGv20170715	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	videoVarFrameSetInfo	VIDEODR2	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	videoVarFrameSetInfo	VIDEODR3	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	videoVarFrameSetInfo	VIDEODR4	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	videoVarFrameSetInfo	VIDEODR5	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	videoVarFrameSetInfo	VIDEOv20100513	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	videoVarFrameSetInfo	VIDEOv20111208	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGDR2	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGDR3	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGDR4	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20110714	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20111019	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20130417	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20140402	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20150421	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20151230	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20160406	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20161202	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vikingVarFrameSetInfo	VIKINGv20170715	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCDR1	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCDR2	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCDR3	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCDR4	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCDR5	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20110816	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20110909	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20120126	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20121128	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20130304	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20130805	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20140428	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20140903	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20150309	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20151218	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20160311	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20160822	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20170109	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20170411	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20171101	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20180702	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20181120	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20191212	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20210708	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20230816	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcVarFrameSetInfo	VMCv20240226	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vvvVarFrameSetInfo	VVVDR5	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vvvVarFrameSetInfo	VVVv20100531	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst	vvvxVarFrameSetInfo	VVVXDR1	Goodness of fit of Strateva function to astrometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqpd	ultravistaMapLcVariability	ULTRAVISTADR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	ultravistaVariability	ULTRAVISTADR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	videoVariability	VIDEODR2	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	videoVariability	VIDEODR3	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	videoVariability	VIDEODR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	videoVariability	VIDEODR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	videoVariability	VIDEOv20100513	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	videoVariability	VIDEOv20111208	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGDR2	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGDR3	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGDR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20110714	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20111019	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20130417	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20140402	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20150421	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20151230	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20160406	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20161202	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vikingVariability	VIKINGv20170715	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCDR1	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCDR2	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCDR3	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCDR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCDR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20110816	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20110909	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20120126	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20121128	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20130304	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20130805	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20140428	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20140903	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20150309	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20151218	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20160311	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20160822	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20170109	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20170411	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20171101	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20180702	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20181120	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20191212	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20210708	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20230816	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcVariability	VMCv20240226	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcdeepVariability	VMCDEEPv20230713	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vmcdeepVariability	VMCDEEPv20240506	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vvvVariability	VVVDR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vvvVariability	VVVv20100531	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd	vvvxVariability	VVVXDR1	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	videoVarFrameSetInfo	VIDEODR2	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	videoVarFrameSetInfo	VIDEODR3	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	videoVarFrameSetInfo	VIDEODR4	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	videoVarFrameSetInfo	VIDEODR5	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	videoVarFrameSetInfo	VIDEOv20100513	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	videoVarFrameSetInfo	VIDEOv20111208	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGDR2	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGDR3	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGDR4	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20110714	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20111019	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20130417	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20140402	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20150421	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20151230	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20160406	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20161202	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vikingVarFrameSetInfo	VIKINGv20170715	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCDR1	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCDR2	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCDR3	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCDR4	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCDR5	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20110816	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20110909	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20120126	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20121128	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20130304	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20130805	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20140428	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20140903	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20150309	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20151218	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20160311	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20160822	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20170109	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20170411	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20171101	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20180702	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20181120	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20191212	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20210708	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20230816	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcVarFrameSetInfo	VMCv20240226	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vvvVarFrameSetInfo	VVVDR5	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vvvVarFrameSetInfo	VVVv20100531	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht	vvvxVarFrameSetInfo	VVVXDR1	Goodness of fit of Strateva function to photometric data in J band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
Jclass	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR5	VVV DR4 J morphological classification. 1 = galaxy,0 = noise,-1 = stellar,-2 = probably stellar,-3 = probable galaxy,-7 = bad pixel within 2" aperture,-9 = saturated {catalogue TType keyword: Jclass}	int	4		-99999999
jClass	ultravistaSource	ULTRAVISTADR4	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	ultravistaSourceRemeasurement	ULTRAVISTADR4	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vhsSource	VHSDR2	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vhsSource	VHSDR3	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSDR4	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSDR5	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSDR6	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20120926	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vhsSource	VHSv20130417	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vhsSource	VHSv20140409	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20150108	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20160114	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20160507	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20170630	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20180419	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20201209	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20231101	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource	VHSv20240731	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vhsSource, vhsSourceRemeasurement	VHSDR1	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	videoSource	VIDEODR2	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	videoSource	VIDEODR3	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	videoSource	VIDEODR4	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	videoSource	VIDEODR5	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	videoSource	VIDEOv20111208	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	videoSource, videoSourceRemeasurement	VIDEOv20100513	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingSource	VIKINGDR2	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingSource	VIKINGDR3	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingSource	VIKINGDR4	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vikingSource	VIKINGv20111019	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingSource	VIKINGv20130417	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingSource	VIKINGv20140402	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingSource	VIKINGv20150421	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vikingSource	VIKINGv20151230	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vikingSource	VIKINGv20160406	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vikingSource	VIKINGv20161202	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vikingSource	VIKINGv20170715	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCDR2	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCDR3	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCDR4	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCDR5	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20110909	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCv20120126	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCv20121128	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCv20130304	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCv20130805	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource	VMCv20140428	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20140903	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20150309	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20151218	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20160311	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20160822	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20170109	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20170411	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20171101	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20180702	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20181120	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20191212	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20210708	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20230816	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource	VMCv20240226	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcSource, vmcSourceRemeasurement	VMCv20110816	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcSource, vmcSynopticSource	VMCDR1	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vmcdeepSource	VMCDEEPv20240506	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vvvSource	VVVDR2	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vvvSource	VVVDR5	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClass	vvvSource	VVVv20110718	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vvvSource, vvvSourceRemeasurement	VVVv20100531	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vvvSource, vvvSynopticSource	VVVDR1	discrete image classification flag in J	smallint	2		-9999	src.class
jClass	vvvxSource	VVVXDR1	discrete image classification flag in J	smallint	2		-9999	src.class;em.IR.J
jClassStat	ultravistaSource	ULTRAVISTADR4	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	ultravistaSourceRemeasurement	ULTRAVISTADR4	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vhsSource	VHSDR2	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vhsSource	VHSDR3	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSDR4	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSDR5	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSDR6	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20120926	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vhsSource	VHSv20130417	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vhsSource	VHSv20140409	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20150108	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20160114	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20160507	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20170630	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20180419	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20201209	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20231101	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource	VHSv20240731	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vhsSource, vhsSourceRemeasurement	VHSDR1	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	videoSource	VIDEODR2	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	videoSource	VIDEODR3	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	videoSource	VIDEODR4	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	videoSource	VIDEODR5	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	videoSource	VIDEOv20100513	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	videoSource	VIDEOv20111208	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	videoSourceRemeasurement	VIDEOv20100513	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingSource	VIKINGDR2	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingSource	VIKINGDR3	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingSource	VIKINGDR4	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vikingSource	VIKINGv20111019	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingSource	VIKINGv20130417	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingSource	VIKINGv20140402	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingSource	VIKINGv20150421	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vikingSource	VIKINGv20151230	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vikingSource	VIKINGv20160406	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vikingSource	VIKINGv20161202	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vikingSource	VIKINGv20170715	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCDR2	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCDR3	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCDR4	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCDR5	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20110909	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCv20120126	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCv20121128	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCv20130304	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCv20130805	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource	VMCv20140428	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20140903	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20150309	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20151218	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20160311	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20160822	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20170109	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20170411	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20171101	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20180702	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20181120	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20191212	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20210708	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20230816	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource	VMCv20240226	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcSource, vmcSourceRemeasurement	VMCv20110816	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcSource, vmcSynopticSource	VMCDR1	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vmcdeepSource	VMCDEEPv20240506	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vvvSource	VVVDR1	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSource	VVVDR2	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jClassStat	vvvSource	VVVv20100531	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSource	VVVv20110718	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSourceRemeasurement	VVVv20100531	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSourceRemeasurement	VVVv20110718	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSynopticSource	VVVDR1	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvSynopticSource	VVVDR2	N(0,1) stellarness-of-profile statistic in J	real	4		-0.9999995e9	stat
jClassStat	vvvxSource	VVVXDR1	S-Extractor classification statistic in J	real	4		-0.9999995e9	stat;em.IR.J
jCorr	twompzPhotoz	TWOMPZ	J 20mag/sq." isophotal fiducial ell. ap. magnitude with Galactic dust correction {image primary HDU keyword: Jcorr}	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jCorrErr	twompzPhotoz	TWOMPZ	J 1-sigma uncertainty in 20mag/sq." aperture {image primary HDU keyword: j_msig_k20fe}	real	4	mag	-0.9999995e9
jcStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGDR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGDR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20130417	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20140402	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20150421	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20151230	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20160406	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20161202	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vikingVarFrameSetInfo	VIKINGv20170715	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGDR3	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGDR4	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20130417	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20140402	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20150421	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20151230	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20160406	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20161202	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vikingVarFrameSetInfo	VIKINGv20170715	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007.	real	4		-0.9999995e9
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band.	real	4		-0.9999995e9	stat.fit.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jdate	twomass_psc	TWOMASS	The Julian Date of the source measurement accurate to +-30 seconds.	float	8	Julian days		time.epoch
jdate	twomass_scn	TWOMASS	Julian Date at beginning of scan.	float	8	Julian days		time.epoch
jdate	twomass_sixx2_psc	TWOMASS	julian date of source measurement to +/- 30 sec	float	8	jdate
jdate	twomass_sixx2_scn	TWOMASS	Julian date beginning UT of scan data	float	8	jdate
jdate	twomass_xsc	TWOMASS	Julian date of the source measurement accurate to +-3 minutes.	float	8	Julian days		time.epoch
jDeblend	vhsSourceRemeasurement	VHSDR1	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	videoSource, videoSourceRemeasurement	VIDEOv20100513	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	vikingSourceRemeasurement	VIKINGv20110714	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	vikingSourceRemeasurement	VIKINGv20111019	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	vmcSourceRemeasurement	VMCv20110816	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	vmcSourceRemeasurement	VMCv20110909	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	vvvSource	VVVv20110718	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
jDeblend	vvvSource, vvvSourceRemeasurement	VVVv20100531	placeholder flag indicating parent/child relation in J	int	4		-99999999	meta.code
Jell	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR5	Ellipticity of the DR4 J detection. {catalogue TType keyword: Jell}	real	4		-999999500.0
jEll	ultravistaSource	ULTRAVISTADR4	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	ultravistaSourceRemeasurement	ULTRAVISTADR4	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticty
jEll	vhsSource	VHSDR2	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vhsSource	VHSDR3	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSDR4	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSDR5	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSDR6	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20120926	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vhsSource	VHSv20130417	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vhsSource	VHSv20140409	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20150108	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20160114	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20160507	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20170630	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20180419	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20201209	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20231101	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource	VHSv20240731	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vhsSource, vhsSourceRemeasurement	VHSDR1	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	videoSource	VIDEODR2	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	videoSource	VIDEODR3	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	videoSource	VIDEODR4	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	videoSource	VIDEODR5	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	videoSource	VIDEOv20111208	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	videoSource, videoSourceRemeasurement	VIDEOv20100513	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingSource	VIKINGDR2	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingSource	VIKINGDR3	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingSource	VIKINGDR4	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vikingSource	VIKINGv20111019	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingSource	VIKINGv20130417	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingSource	VIKINGv20140402	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingSource	VIKINGv20150421	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vikingSource	VIKINGv20151230	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vikingSource	VIKINGv20160406	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vikingSource	VIKINGv20161202	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vikingSource	VIKINGv20170715	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCDR2	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCDR3	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCDR4	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCDR5	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20110909	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCv20120126	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCv20121128	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCv20130304	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCv20130805	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource	VMCv20140428	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20140903	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20150309	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20151218	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20160311	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20160822	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20170109	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20170411	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20171101	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20180702	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20181120	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20191212	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20210708	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20230816	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource	VMCv20240226	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcSource, vmcSourceRemeasurement	VMCv20110816	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcSource, vmcSynopticSource	VMCDR1	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vmcdeepSource	VMCDEEPv20240506	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vvvSource	VVVDR2	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jEll	vvvSource	VVVv20110718	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vvvSource, vvvSourceRemeasurement	VVVv20100531	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vvvSource, vvvSynopticSource	VVVDR1	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity
jEll	vvvxSource	VVVXDR1	1-b/a, where a/b=semi-major/minor axes in J	real	4		-0.9999995e9	src.ellipticity;em.IR.J
jeNum	ultravistaMergeLog, ultravistaRemeasMergeLog	ULTRAVISTADR4	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSDR1	the extension number of this J frame	tinyint	1			meta.number
jeNum	vhsMergeLog	VHSDR2	the extension number of this J frame	tinyint	1			meta.number
jeNum	vhsMergeLog	VHSDR3	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSDR4	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSDR5	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSDR6	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20120926	the extension number of this J frame	tinyint	1			meta.number
jeNum	vhsMergeLog	VHSv20130417	the extension number of this J frame	tinyint	1			meta.number
jeNum	vhsMergeLog	VHSv20140409	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20150108	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20160114	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20160507	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20170630	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20180419	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vhsMergeLog	VHSv20201209	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vhsMergeLog	VHSv20231101	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vhsMergeLog	VHSv20240731	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	videoMergeLog	VIDEODR2	the extension number of this J frame	tinyint	1			meta.number
jeNum	videoMergeLog	VIDEODR3	the extension number of this J frame	tinyint	1			meta.number
jeNum	videoMergeLog	VIDEODR4	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	videoMergeLog	VIDEODR5	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	videoMergeLog	VIDEOv20100513	the extension number of this J frame	tinyint	1			meta.number
jeNum	videoMergeLog	VIDEOv20111208	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGDR2	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGDR3	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGDR4	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vikingMergeLog	VIKINGv20110714	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGv20111019	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGv20130417	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGv20140402	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingMergeLog	VIKINGv20150421	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vikingMergeLog	VIKINGv20151230	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vikingMergeLog	VIKINGv20160406	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vikingMergeLog	VIKINGv20161202	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vikingMergeLog	VIKINGv20170715	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	the extension number of this J frame	tinyint	1			meta.number
jeNum	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20170124	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCDR2	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCDR3	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCDR4	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCDR5	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vmcMergeLog	VMCv20110816	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCv20110909	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCv20120126	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCv20121128	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCv20130304	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCv20130805	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcMergeLog	VMCv20140428	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20140903	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20150309	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20151218	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20160311	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20160822	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20170109	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20170411	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20171101	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20180702	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20181120	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vmcMergeLog	VMCv20191212	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vmcMergeLog	VMCv20210708	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vmcMergeLog	VMCv20230816	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vmcMergeLog	VMCv20240226	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vmcMergeLog, vmcSynopticMergeLog	VMCDR1	the extension number of this J frame	tinyint	1			meta.number
jeNum	vmcdeepMergeLog	VMCDEEPv20240506	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vmcdeepMergeLog, vmcdeepSynopticMergeLog	VMCDEEPv20230713	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jeNum	vvvMergeLog	VVVDR2	the extension number of this J frame	tinyint	1			meta.number
jeNum	vvvMergeLog	VVVv20100531	the extension number of this J frame	tinyint	1			meta.number
jeNum	vvvMergeLog	VVVv20110718	the extension number of this J frame	tinyint	1			meta.number
jeNum	vvvMergeLog, vvvPsfDaophotJKsMergeLog	VVVDR5	the extension number of this J frame	tinyint	1			meta.number;em.IR.J
jeNum	vvvMergeLog, vvvSynopticMergeLog	VVVDR1	the extension number of this J frame	tinyint	1			meta.number
jeNum	vvvxMergeLog	VVVXDR1	the extension number of this J frame	tinyint	1			meta.id;em.IR.J
jErrBits	ultravistaSource	ULTRAVISTADR4	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	ultravistaSourceRemeasurement	ULTRAVISTADR4	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSDR1	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSDR2	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSDR3	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSDR4	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSDR5	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSDR6	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20120926	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20130417	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20140409	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20150108	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20160114	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20160507	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20170630	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20180419	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20201209	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20231101	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSource	VHSv20240731	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vhsSourceRemeasurement	VHSDR1	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	videoSource	VIDEODR2	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	videoSource	VIDEODR3	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	videoSource	VIDEODR4	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	videoSource	VIDEODR5	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	videoSource	VIDEOv20100513	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	videoSource	VIDEOv20111208	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits	videoSourceRemeasurement	VIDEOv20100513	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vikingSource	VIKINGDR2	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGDR3	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGDR4	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20110714	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20111019	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20130417	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20140402	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20150421	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20151230	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20160406	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20161202	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSource	VIKINGv20170715	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingSourceRemeasurement	VIKINGv20110714	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vikingSourceRemeasurement	VIKINGv20111019	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCDR2	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCDR3	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCDR4	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCDR5	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20110816	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20110909	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20120126	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20121128	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20130304	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20130805	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20140428	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20140903	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20150309	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20151218	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20160311	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20160822	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20170109	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20170411	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20171101	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20180702	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20181120	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20191212	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20210708	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20230816	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource	VMCv20240226	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSource, vmcSynopticSource	VMCDR1	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcSourceRemeasurement	VMCv20110816	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vmcSourceRemeasurement	VMCv20110909	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vmcdeepSource	VMCDEEPv20240506	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vvvSource	VVVDR2	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vvvSource	VVVv20100531	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vvvSource	VVVv20110718	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vvvSource, vvvSynopticSource	VVVDR1	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits	vvvSourceRemeasurement	VVVv20100531	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vvvSourceRemeasurement	VVVv20110718	processing warning/error bitwise flags in J	int	4		-99999999	meta.code
jErrBits	vvvxSource	VVVXDR1	processing warning/error bitwise flags in J	int	4		-99999999	meta.code;em.IR.J
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jEta	ultravistaSource	ULTRAVISTADR4	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSDR1	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSDR2	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSDR3	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSDR4	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSDR5	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSDR6	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20120926	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20130417	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20140409	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20150108	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20160114	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20160507	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20170630	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20180419	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20201209	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20231101	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vhsSource	VHSv20240731	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	videoSource	VIDEODR2	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	videoSource	VIDEODR3	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	videoSource	VIDEODR4	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	videoSource	VIDEODR5	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	videoSource	VIDEOv20100513	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	videoSource	VIDEOv20111208	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGDR2	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGDR3	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGDR4	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20110714	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20111019	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20130417	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20140402	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20150421	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20151230	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20160406	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20161202	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vikingSource	VIKINGv20170715	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCDR2	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCDR3	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCDR4	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCDR5	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20110816	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20110909	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20120126	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20121128	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20130304	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20130805	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20140428	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20140903	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20150309	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20151218	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20160311	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20160822	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20170109	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20170411	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20171101	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20180702	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20181120	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20191212	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20210708	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20230816	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource	VMCv20240226	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcSource, vmcSynopticSource	VMCDR1	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcdeepSource	VMCDEEPv20240506	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vvvSource	VVVDR2	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vvvSource	VVVDR5	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vvvSource	VVVv20100531	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vvvSource	VVVv20110718	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vvvSource, vvvSynopticSource	VVVDR1	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta	vvvxSource	VVVXDR1	Offset of J detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jexpML	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	videoVarFrameSetInfo	VIDEODR2	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	videoVarFrameSetInfo	VIDEODR3	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9	phot.mag;stat.max;em.IR.NIR
jexpML	videoVarFrameSetInfo	VIDEODR4	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	videoVarFrameSetInfo	VIDEODR5	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	videoVarFrameSetInfo	VIDEOv20100513	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	videoVarFrameSetInfo	VIDEOv20111208	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vikingVarFrameSetInfo	VIKINGDR2	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vikingVarFrameSetInfo	VIKINGDR3	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
jexpML	vikingVarFrameSetInfo	VIKINGDR4	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vikingVarFrameSetInfo	VIKINGv20110714	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vikingVarFrameSetInfo	VIKINGv20111019	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vikingVarFrameSetInfo	VIKINGv20130417	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
jexpML	vikingVarFrameSetInfo	VIKINGv20140402	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max;em.IR.NIR
jexpML	vikingVarFrameSetInfo	VIKINGv20150421	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vikingVarFrameSetInfo	VIKINGv20151230	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vikingVarFrameSetInfo	VIKINGv20160406	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vikingVarFrameSetInfo	VIKINGv20161202	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vikingVarFrameSetInfo	VIKINGv20170715	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCDR1	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vmcVarFrameSetInfo	VMCDR2	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max;em.IR.NIR
jexpML	vmcVarFrameSetInfo	VMCDR3	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCDR4	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCDR5	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20110816	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vmcVarFrameSetInfo	VMCv20110909	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vmcVarFrameSetInfo	VMCv20120126	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vmcVarFrameSetInfo	VMCv20121128	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
jexpML	vmcVarFrameSetInfo	VMCv20130304	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
jexpML	vmcVarFrameSetInfo	VMCv20130805	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max;em.IR.NIR
jexpML	vmcVarFrameSetInfo	VMCv20140428	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20140903	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20150309	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20151218	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20160311	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20160822	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20170109	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20170411	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20171101	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20180702	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20181120	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20191212	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20210708	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20230816	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcVarFrameSetInfo	VMCv20240226	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vvvVarFrameSetInfo	VVVDR5	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jexpML	vvvVarFrameSetInfo	VVVv20100531	Expected magnitude limit of frameSet in this in J band.	real	4		-0.9999995e9
jexpML	vvvxVarFrameSetInfo	VVVXDR1	Expected magnitude limit of frameSet in this in J band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
jExpRms	ultravistaMapLcVariability	ULTRAVISTADR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	ultravistaVariability	ULTRAVISTADR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	videoVariability	VIDEODR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	videoVariability	VIDEODR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	videoVariability	VIDEODR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	videoVariability	VIDEODR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	videoVariability	VIDEOv20100513	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	videoVariability	VIDEOv20111208	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGDR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGDR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGDR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20110714	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20111019	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20130417	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20140402	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20150421	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20151230	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20160406	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20161202	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vikingVariability	VIKINGv20170715	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCDR1	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCDR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCDR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCDR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCDR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20110816	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20110909	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20120126	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20121128	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20130304	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20130805	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20140428	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20140903	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20150309	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20151218	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20160311	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20160822	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20170109	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20170411	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20171101	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20180702	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20181120	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20191212	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20210708	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20230816	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcVariability	VMCv20240226	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcdeepVariability	VMCDEEPv20230713	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vmcdeepVariability	VMCDEEPv20240506	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vvvVariability	VVVDR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vvvVariability	VVVv20100531	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms	vvvxVariability	VVVXDR1	Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jGausig	ultravistaSource	ULTRAVISTADR4	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	ultravistaSourceRemeasurement	ULTRAVISTADR4	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vhsSource	VHSDR2	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vhsSource	VHSDR3	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSDR4	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSDR5	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSDR6	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20120926	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vhsSource	VHSv20130417	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vhsSource	VHSv20140409	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20150108	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20160114	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20160507	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20170630	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20180419	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20201209	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20231101	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource	VHSv20240731	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vhsSource, vhsSourceRemeasurement	VHSDR1	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	videoSource	VIDEODR2	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	videoSource	VIDEODR3	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	videoSource	VIDEODR4	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	videoSource	VIDEODR5	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	videoSource	VIDEOv20111208	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	videoSource, videoSourceRemeasurement	VIDEOv20100513	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingSource	VIKINGDR2	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingSource	VIKINGDR3	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingSource	VIKINGDR4	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vikingSource	VIKINGv20111019	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingSource	VIKINGv20130417	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingSource	VIKINGv20140402	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingSource	VIKINGv20150421	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vikingSource	VIKINGv20151230	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vikingSource	VIKINGv20160406	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vikingSource	VIKINGv20161202	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vikingSource	VIKINGv20170715	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCDR2	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCDR3	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCDR4	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCDR5	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20110909	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCv20120126	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCv20121128	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCv20130304	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCv20130805	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource	VMCv20140428	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20140903	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20150309	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20151218	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20160311	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20160822	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20170109	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20170411	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20171101	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20180702	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20181120	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20191212	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20210708	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20230816	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource	VMCv20240226	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcSource, vmcSourceRemeasurement	VMCv20110816	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcSource, vmcSynopticSource	VMCDR1	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vmcdeepSource	VMCDEEPv20240506	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vvvSource	VVVDR2	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jGausig	vvvSource	VVVv20110718	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vvvSource, vvvSourceRemeasurement	VVVv20100531	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vvvSource, vvvSynopticSource	VVVDR1	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param
jGausig	vvvxSource	VVVXDR1	RMS of axes of ellipse fit in J	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.J
jHalfRad	ultravistaSource	ULTRAVISTADR4	SExtractor half-light radius in J band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.J
jHalfRad	videoSource	VIDEODR4	SExtractor half-light radius in J band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.J
jHalfRad	videoSource	VIDEODR5	SExtractor half-light radius in J band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	ultravistaSource	ULTRAVISTADR4	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	ultravistaSourceRemeasurement	ULTRAVISTADR4	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	vhsSource	VHSDR1	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	vhsSource	VHSDR2	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	vhsSource	VHSDR3	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSDR4	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSDR5	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSDR6	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20120926	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	vhsSource	VHSv20130417	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	vhsSource	VHSv20140409	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20150108	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20160114	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20160507	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20170630	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20180419	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20201209	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20231101	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vhsSource	VHSv20240731	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	videoSource	VIDEODR2	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	videoSource	VIDEODR3	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	videoSource	VIDEODR4	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	videoSource	VIDEODR5	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	videoSource	VIDEOv20100513	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	videoSource	VIDEOv20111208	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	vikingSource	VIKINGDR2	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	vikingSource	VIKINGDR3	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	vikingSource	VIKINGDR4	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vikingSource	VIKINGv20110714	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	vikingSource	VIKINGv20111019	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;src
jHlCorSMjRadAs	vikingSource	VIKINGv20130417	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	vikingSource	VIKINGv20140402	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize
jHlCorSMjRadAs	vikingSource	VIKINGv20150421	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vikingSource	VIKINGv20151230	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vikingSource	VIKINGv20160406	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vikingSource	VIKINGv20161202	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jHlCorSMjRadAs	vikingSource	VIKINGv20170715	Seeing corrected half-light, semi-major axis in J band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.J
jIntRms	ultravistaMapLcVariability	ULTRAVISTADR4	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	ultravistaVariability	ULTRAVISTADR4	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	videoVariability	VIDEODR2	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	videoVariability	VIDEODR3	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	videoVariability	VIDEODR4	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	videoVariability	VIDEODR5	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	videoVariability	VIDEOv20100513	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	videoVariability	VIDEOv20111208	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGDR2	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGDR3	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGDR4	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20110714	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20111019	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20130417	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20140402	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20150421	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20151230	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20160406	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20161202	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vikingVariability	VIKINGv20170715	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCDR1	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCDR2	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCDR3	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCDR4	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCDR5	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20110816	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20110909	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20120126	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20121128	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20130304	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20130805	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20140428	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20140903	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20150309	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20151218	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20160311	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20160822	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20170109	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20170411	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20171101	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20180702	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20181120	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20191212	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20210708	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20230816	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcVariability	VMCv20240226	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcdeepVariability	VMCDEEPv20230713	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vmcdeepVariability	VMCDEEPv20240506	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vvvVariability	VVVDR5	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vvvVariability	VVVv20100531	Intrinsic rms in J-band	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms	vvvxVariability	VVVXDR1	Intrinsic rms in J-band	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jisDefAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	videoVarFrameSetInfo	VIDEODR2	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	videoVarFrameSetInfo	VIDEODR3	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	videoVarFrameSetInfo	VIDEODR4	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	videoVarFrameSetInfo	VIDEODR5	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	videoVarFrameSetInfo	VIDEOv20111208	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vikingVarFrameSetInfo	VIKINGDR2	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vikingVarFrameSetInfo	VIKINGDR3	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vikingVarFrameSetInfo	VIKINGDR4	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vikingVarFrameSetInfo	VIKINGv20111019	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vikingVarFrameSetInfo	VIKINGv20130417	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vikingVarFrameSetInfo	VIKINGv20140402	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vikingVarFrameSetInfo	VIKINGv20150421	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vikingVarFrameSetInfo	VIKINGv20151230	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vikingVarFrameSetInfo	VIKINGv20160406	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vikingVarFrameSetInfo	VIKINGv20161202	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vikingVarFrameSetInfo	VIKINGv20170715	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCDR1	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vmcVarFrameSetInfo	VMCDR2	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vmcVarFrameSetInfo	VMCDR3	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCDR4	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCDR5	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20110816	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vmcVarFrameSetInfo	VMCv20110909	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vmcVarFrameSetInfo	VMCv20120126	Use a default model for the astrometric noise in J band.	tinyint	1		0
jisDefAst	vmcVarFrameSetInfo	VMCv20121128	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vmcVarFrameSetInfo	VMCv20130304	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vmcVarFrameSetInfo	VMCv20130805	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefAst	vmcVarFrameSetInfo	VMCv20140428	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20140903	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20150309	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20151218	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20160311	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20160822	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20170109	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20170411	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20171101	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20180702	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20181120	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20191212	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20210708	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20230816	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcVarFrameSetInfo	VMCv20240226	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vvvVarFrameSetInfo	VVVDR5	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefAst	vvvxVarFrameSetInfo	VVVXDR1	Use a default model for the astrometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	videoVarFrameSetInfo	VIDEODR2	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	videoVarFrameSetInfo	VIDEODR3	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	videoVarFrameSetInfo	VIDEODR4	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	videoVarFrameSetInfo	VIDEODR5	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	videoVarFrameSetInfo	VIDEOv20111208	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vikingVarFrameSetInfo	VIKINGDR2	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vikingVarFrameSetInfo	VIKINGDR3	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vikingVarFrameSetInfo	VIKINGDR4	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vikingVarFrameSetInfo	VIKINGv20111019	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vikingVarFrameSetInfo	VIKINGv20130417	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vikingVarFrameSetInfo	VIKINGv20140402	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vikingVarFrameSetInfo	VIKINGv20150421	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vikingVarFrameSetInfo	VIKINGv20151230	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vikingVarFrameSetInfo	VIKINGv20160406	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vikingVarFrameSetInfo	VIKINGv20161202	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vikingVarFrameSetInfo	VIKINGv20170715	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCDR1	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vmcVarFrameSetInfo	VMCDR2	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vmcVarFrameSetInfo	VMCDR3	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCDR4	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCDR5	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20110816	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vmcVarFrameSetInfo	VMCv20110909	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vmcVarFrameSetInfo	VMCv20120126	Use a default model for the photometric noise in J band.	tinyint	1		0
jisDefPht	vmcVarFrameSetInfo	VMCv20121128	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vmcVarFrameSetInfo	VMCv20130304	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vmcVarFrameSetInfo	VMCv20130805	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.NIR
jisDefPht	vmcVarFrameSetInfo	VMCv20140428	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20140903	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20150309	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20151218	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20160311	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20160822	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20170109	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20170411	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20171101	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20180702	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20181120	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20191212	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20210708	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20230816	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcVarFrameSetInfo	VMCv20240226	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vvvVarFrameSetInfo	VVVDR5	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jisDefPht	vvvxVarFrameSetInfo	VVVXDR1	Use a default model for the photometric noise in J band.	tinyint	1		0	meta.code;em.IR.J
jIsMeas	ultravistaSourceRemeasurement	ULTRAVISTADR4	Is pass band J measured? 0 no, 1 yes	tinyint	1		0	meta.code
jIsMeas	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Is pass band J measured? 0 no, 1 yes	tinyint	1		0	meta.code
jIsMeas	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Is pass band J measured? 0 no, 1 yes	tinyint	1		0	meta.code
jitterID	Multiframe	SHARKSv20210222	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	SHARKSv20210421	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	ULTRAVISTADR4	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSDR1	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSDR2	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSDR3	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSDR4	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSDR5	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSDR6	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20120926	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20130417	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20140409	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20150108	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20160114	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20160507	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20170630	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20180419	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20201209	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20231101	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VHSv20240731	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIDEODR2	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIDEODR3	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIDEODR4	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIDEODR5	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIDEOv20100513	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIDEOv20111208	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGDR2	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGDR3	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGDR4	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20110714	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20111019	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20130417	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20140402	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20150421	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20151230	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20160406	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20161202	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VIKINGv20170715	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDEEPv20230713	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDEEPv20240506	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDR1	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDR2	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDR3	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDR4	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCDR5	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20110816	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20110909	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20120126	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20121128	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20130304	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20130805	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20140428	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20140903	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20150309	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20151218	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20160311	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20160822	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20170109	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20170411	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20171101	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20180702	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20181120	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20191212	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20210708	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20230816	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VMCv20240226	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VVVDR1	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VVVDR2	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VVVDR5	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VVVXDR1	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VVVv20100531	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	Multiframe	VVVv20110718	Sequence number of jitter {image primary HDU keyword: JITTER_I}	smallint	2		-9999
jitterID	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	Sequence number of jitter	smallint	2		-9999
jitterName	Multiframe	SHARKSv20210222	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	SHARKSv20210421	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	ULTRAVISTADR4	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSDR1	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSDR2	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSDR3	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSDR4	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSDR5	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSDR6	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20120926	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20130417	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20140409	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20150108	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20160114	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20160507	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20170630	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20180419	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20201209	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20231101	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VHSv20240731	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIDEODR2	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIDEODR3	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIDEODR4	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIDEODR5	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIDEOv20100513	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIDEOv20111208	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGDR2	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGDR3	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGDR4	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20110714	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20111019	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20130417	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20140402	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20150421	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20151230	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20160406	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20161202	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VIKINGv20170715	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDEEPv20230713	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDEEPv20240506	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDR1	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDR2	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDR3	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDR4	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCDR5	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20110816	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20110909	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20120126	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20121128	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20130304	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20130805	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20140428	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20140903	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20150309	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20151218	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20160311	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20160822	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20170109	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20170411	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20171101	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20180702	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20181120	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20191212	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20210708	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20230816	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VMCv20240226	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VVVDR1	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VVVDR2	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VVVDR5	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VVVXDR1	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VVVv20100531	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	Multiframe	VVVv20110718	Name of jitter pattern {image primary HDU keyword: JITTR_ID}	varchar	8		NONE
jitterName	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	Name of jitter pattern	varchar	8		NONE
jitterX	Multiframe	SHARKSv20210222	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	SHARKSv20210421	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	ULTRAVISTADR4	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSDR1	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSDR2	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSDR3	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSDR4	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSDR5	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSDR6	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20120926	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20130417	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20140409	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20150108	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20160114	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20160507	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20170630	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20180419	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20201209	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20231101	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VHSv20240731	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIDEODR2	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIDEODR3	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIDEODR4	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIDEODR5	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIDEOv20100513	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIDEOv20111208	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGDR2	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGDR3	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGDR4	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20110714	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20111019	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20130417	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20140402	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20150421	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20151230	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20160406	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20161202	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VIKINGv20170715	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDEEPv20230713	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDEEPv20240506	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDR1	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDR2	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDR3	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDR4	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCDR5	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20110816	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20110909	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20120126	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20121128	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20130304	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20130805	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20140428	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20140903	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20150309	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20151218	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20160311	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20160822	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20170109	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20170411	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20171101	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20180702	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20181120	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20191212	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20210708	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20230816	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VMCv20240226	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VVVDR1	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VVVDR2	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VVVDR5	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VVVXDR1	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VVVv20100531	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	Multiframe	VVVv20110718	X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X}	real	4		-0.9999995e9
jitterX	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	X offset in jitter pattern [arcsec]	real	4		-0.9999995e9
jitterY	Multiframe	SHARKSv20210222	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	SHARKSv20210421	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	ULTRAVISTADR4	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSDR1	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSDR2	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSDR3	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSDR4	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSDR5	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSDR6	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20120926	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20130417	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20140409	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20150108	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20160114	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20160507	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20170630	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20180419	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20201209	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20231101	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VHSv20240731	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIDEODR2	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIDEODR3	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIDEODR4	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIDEODR5	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIDEOv20100513	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIDEOv20111208	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGDR2	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGDR3	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGDR4	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20110714	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20111019	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20130417	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20140402	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20150421	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20151230	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20160406	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20161202	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VIKINGv20170715	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDEEPv20230713	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDEEPv20240506	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDR1	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDR2	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDR3	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDR4	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCDR5	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20110816	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20110909	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20120126	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20121128	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20130304	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20130805	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20140428	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20140903	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20150309	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20151218	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20160311	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20160822	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20170109	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20170411	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20171101	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20180702	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20181120	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20191212	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20210708	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20230816	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VMCv20240226	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VVVDR1	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VVVDR2	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VVVDR5	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VVVXDR1	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VVVv20100531	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	Multiframe	VVVv20110718	Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y}	real	4		-0.9999995e9
jitterY	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	Y offset in jitter pattern [arcsec]	real	4		-0.9999995e9
jKronJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J calibrated flux (Kron)	real	4	jansky	-0.9999995e9	phot.flux
jKronJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source J calibrated flux (Kron)	real	4	janksy	-0.9999995e9	stat.error
jKronLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J luptitude (Kron)	real	4	lup	-0.9999995e9	phot.lup
jKronLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source J luptitude (Kron)	real	4	lup	-0.9999995e9	stat.error
jKronMag	ultravistaSource	ULTRAVISTADR4	Extended source J mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jKronMag	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J magnitude (Kron)	real	4	mag	-0.9999995e9	phot.mag
jKronMag	videoSource	VIDEODR4	Extended source J mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jKronMag	videoSource	VIDEODR5	Extended source J mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jKronMagErr	ultravistaSource	ULTRAVISTADR4	Extended source J mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jKronMagErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source J magnitude (Kron)	real	4	mag	-0.9999995e9	stat.error
jKronMagErr	videoSource	VIDEODR4	Extended source J mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jKronMagErr	videoSource	VIDEODR5	Extended source J mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jksiWS	vmcVariability	VMCDR1	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCDR2	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCDR3	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCDR4	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCDR5	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20110816	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20110909	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20120126	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20121128	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20130304	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20130805	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20140428	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20140903	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20150309	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20151218	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20160311	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20160822	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20170109	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20170411	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20171101	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20180702	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20181120	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20191212	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20210708	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20230816	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcVariability	VMCv20240226	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcdeepVariability	VMCDEEPv20230713	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS	vmcdeepVariability	VMCDEEPv20240506	Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections	real	4		-0.9999995e9	stat.param;em.IR.J;em.IR.K
			The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
Jmag	mcps_lmcSource, mcps_smcSource	MCPS	The J band magnitude (from 2MASS) (0.00 if star not detected.)	real	4	mag
Jmag	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR5	VVV DR4 J photometry {catalogue TType keyword: Jmag}	real	4	mag	-999999500.0
jMag	ukirtFSstars	VIDEOv20100513	J band total magnitude on the MKO(UFTI) system	real	4	mag		phot.mag
jMag	ukirtFSstars	VIKINGv20110714	J band total magnitude on the MKO(UFTI) system	real	4	mag		phot.mag
jMag	ukirtFSstars	VVVv20100531	J band total magnitude on the MKO(UFTI) system	real	4	mag		phot.mag
jMag	vhsSourceRemeasurement	VHSDR1	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	videoSourceRemeasurement	VIDEOv20100513	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	vikingSourceRemeasurement	VIKINGv20110714	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	vikingSourceRemeasurement	VIKINGv20111019	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	vmcSourceRemeasurement	VMCv20110816	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	vmcSourceRemeasurement	VMCv20110909	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	vvvSourceRemeasurement	VVVv20100531	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
jMag	vvvSourceRemeasurement	VVVv20110718	J mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
Jmag2MASS	spitzer_smcSource	SPITZER	The 2MASS J band magnitude.	real	4	mag
Jmag_2MASS	ravedr5Source	RAVE	J selected default magnitude from 2MASS	real	4	mag	magnitude	phot.mag;em.IR.J
Jmag_DENIS	ravedr5Source	RAVE	J selected default magnitude from DENIS	real	4	mag	magnitude	phot.mag;em.IR.J
jMagErr	ukirtFSstars	VIDEOv20100513	J band magnitude error	real	4	mag		stat.error
jMagErr	ukirtFSstars	VIKINGv20110714	J band magnitude error	real	4	mag		stat.error
jMagErr	ukirtFSstars	VVVv20100531	J band magnitude error	real	4	mag		stat.error
jMagErr	vhsSourceRemeasurement	VHSDR1	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	videoSourceRemeasurement	VIDEOv20100513	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	vikingSourceRemeasurement	VIKINGv20110714	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	vikingSourceRemeasurement	VIKINGv20111019	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	vmcCepheidVariables	VMCDR4	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20160311	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20160822	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20170109	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20170411	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20171101	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20180702	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20181120	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20191212	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20210708	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20230816	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcCepheidVariables	VMCv20240226	Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jMagErr	vmcSourceRemeasurement	VMCv20110816	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	vmcSourceRemeasurement	VMCv20110909	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	vvvSourceRemeasurement	VVVv20100531	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagErr	vvvSourceRemeasurement	VVVv20110718	Error in J mag	real	4	mag	-0.9999995e9	stat.error
jMagMAD	ultravistaMapLcVariability	ULTRAVISTADR4	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	ultravistaVariability	ULTRAVISTADR4	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	videoVariability	VIDEODR2	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	videoVariability	VIDEODR3	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	videoVariability	VIDEODR4	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	videoVariability	VIDEODR5	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	videoVariability	VIDEOv20100513	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	videoVariability	VIDEOv20111208	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGDR2	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGDR3	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGDR4	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20110714	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20111019	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20130417	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20140402	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20150421	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20151230	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20160406	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20161202	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vikingVariability	VIKINGv20170715	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCDR1	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCDR2	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCDR3	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCDR4	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCDR5	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20110816	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20110909	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20120126	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20121128	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20130304	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20130805	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20140428	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20140903	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20150309	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20151218	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20160311	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20160822	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20170109	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20170411	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20171101	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20180702	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20181120	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20191212	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20210708	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20230816	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcVariability	VMCv20240226	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcdeepVariability	VMCDEEPv20230713	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vmcdeepVariability	VMCDEEPv20240506	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vvvVariability	VVVDR5	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vvvVariability	VVVv20100531	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD	vvvxVariability	VVVXDR1	Median Absolute Deviation of J magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	ultravistaMapLcVariability	ULTRAVISTADR4	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	ultravistaVariability	ULTRAVISTADR4	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	videoVariability	VIDEODR2	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	videoVariability	VIDEODR3	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	videoVariability	VIDEODR4	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	videoVariability	VIDEODR5	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	videoVariability	VIDEOv20100513	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	videoVariability	VIDEOv20111208	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGDR2	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGDR3	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGDR4	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20110714	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20111019	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20130417	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20140402	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20150421	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20151230	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20160406	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20161202	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vikingVariability	VIKINGv20170715	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCDR1	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCDR2	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCDR3	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCDR4	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCDR5	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20110816	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20110909	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20120126	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20121128	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20130304	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20130805	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20140428	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20140903	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20150309	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20151218	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20160311	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20160822	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20170109	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20170411	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20171101	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20180702	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20181120	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20191212	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20210708	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20230816	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcVariability	VMCv20240226	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcdeepVariability	VMCDEEPv20230713	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vmcdeepVariability	VMCDEEPv20240506	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vvvVariability	VVVDR5	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vvvVariability	VVVv20100531	rms of J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms	vvvxVariability	VVVXDR1	rms of J magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmaxCadence	ultravistaMapLcVariability	ULTRAVISTADR4	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	ultravistaVariability	ULTRAVISTADR4	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	videoVariability	VIDEODR2	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	videoVariability	VIDEODR3	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	videoVariability	VIDEODR4	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	videoVariability	VIDEODR5	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	videoVariability	VIDEOv20100513	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	videoVariability	VIDEOv20111208	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGDR2	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGDR3	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGDR4	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20110714	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20111019	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20130417	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20140402	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20150421	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20151230	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20160406	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20161202	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vikingVariability	VIKINGv20170715	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCDR1	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCDR2	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCDR3	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCDR4	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCDR5	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20110816	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20110909	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20120126	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20121128	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20130304	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20130805	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20140428	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20140903	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20150309	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20151218	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20160311	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20160822	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20170109	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20170411	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20171101	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20180702	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20181120	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20191212	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20210708	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20230816	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcVariability	VMCv20240226	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcdeepVariability	VMCDEEPv20230713	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vmcdeepVariability	VMCDEEPv20240506	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vvvVariability	VVVDR5	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vvvVariability	VVVv20100531	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence	vvvxVariability	VVVXDR1	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jMaxMag	ultravistaMapLcVariability	ULTRAVISTADR4	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	ultravistaVariability	ULTRAVISTADR4	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	videoVariability	VIDEODR2	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	videoVariability	VIDEODR3	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9	phot.mag;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	videoVariability	VIDEODR4	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	videoVariability	VIDEODR5	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	videoVariability	VIDEOv20100513	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	videoVariability	VIDEOv20111208	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGDR2	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGDR3	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGDR4	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20110714	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20111019	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20130417	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20140402	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20150421	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20151230	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20160406	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20161202	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vikingVariability	VIKINGv20170715	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCDR1	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCDR2	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCDR3	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCDR4	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCDR5	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20110816	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20110909	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20120126	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20121128	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20130304	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20130805	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20140428	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20140903	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20150309	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20151218	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20160311	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20160822	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20170109	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20170411	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20171101	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20180702	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20181120	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20191212	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20210708	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20230816	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcVariability	VMCv20240226	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcdeepVariability	VMCDEEPv20230713	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vmcdeepVariability	VMCDEEPv20240506	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vvvVariability	VVVDR5	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vvvVariability	VVVv20100531	Maximum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag	vvvxVariability	VVVXDR1	Maximum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.max
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMean	vmcRRLyraeVariables	VMCv20240226	Mean J band magnitude derived with GRATIS {catalogue TType keyword: J_MEAN}	real	4	mag		phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCDR4	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20160311	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20160822	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20170109	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20170411	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20171101	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20180702	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20181120	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20191212	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20210708	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20230816	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jMeanMag	vmcCepheidVariables	VMCv20240226	Intensity-averaged J band magnitude {catalogue TType keyword: Jmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.J
jmeanMag	ultravistaMapLcVariability	ULTRAVISTADR4	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	ultravistaVariability	ULTRAVISTADR4	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	videoVariability	VIDEODR2	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	videoVariability	VIDEODR3	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	videoVariability	VIDEODR4	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	videoVariability	VIDEODR5	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	videoVariability	VIDEOv20100513	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	videoVariability	VIDEOv20111208	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGDR2	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGDR3	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGDR4	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20110714	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20111019	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20130417	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20140402	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20150421	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20151230	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20160406	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20161202	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vikingVariability	VIKINGv20170715	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCDR1	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCDR2	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCDR3	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCDR4	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCDR5	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20110816	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20110909	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20120126	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20121128	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20130304	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20130805	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20140428	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20140903	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20150309	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20151218	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20160311	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20160822	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20170109	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20170411	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20171101	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20180702	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20181120	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20191212	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20210708	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20230816	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcVariability	VMCv20240226	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcdeepVariability	VMCDEEPv20230713	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vmcdeepVariability	VMCDEEPv20240506	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vvvVariability	VVVDR5	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vvvVariability	VVVv20100531	Mean J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag	vvvxVariability	VVVXDR1	Mean J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.mean;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
Jmeas	vvvProperMotionCatalogue	VVVDR5	Is there a J band measurment for this frame	tinyint	1
jmedCadence	ultravistaMapLcVariability	ULTRAVISTADR4	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	ultravistaVariability	ULTRAVISTADR4	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	videoVariability	VIDEODR2	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	videoVariability	VIDEODR3	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	videoVariability	VIDEODR4	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	videoVariability	VIDEODR5	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	videoVariability	VIDEOv20100513	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	videoVariability	VIDEOv20111208	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGDR2	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGDR3	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGDR4	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20110714	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20111019	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20130417	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20140402	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20150421	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20151230	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20160406	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20161202	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vikingVariability	VIKINGv20170715	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCDR1	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCDR2	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCDR3	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCDR4	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCDR5	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20110816	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20110909	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20120126	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20121128	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20130304	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20130805	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20140428	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20140903	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20150309	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20151218	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20160311	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20160822	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20170109	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20170411	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20171101	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20180702	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20181120	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20191212	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20210708	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20230816	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcVariability	VMCv20240226	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcdeepVariability	VMCDEEPv20230713	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vmcdeepVariability	VMCDEEPv20240506	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vvvVariability	VVVDR5	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vvvVariability	VVVv20100531	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence	vvvxVariability	VVVXDR1	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedianMag	ultravistaMapLcVariability	ULTRAVISTADR4	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	ultravistaVariability	ULTRAVISTADR4	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	videoVariability	VIDEODR2	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	videoVariability	VIDEODR3	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	videoVariability	VIDEODR4	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	videoVariability	VIDEODR5	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	videoVariability	VIDEOv20100513	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	videoVariability	VIDEOv20111208	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGDR2	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGDR3	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGDR4	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20110714	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20111019	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20130417	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20140402	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20150421	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20151230	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20160406	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20161202	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vikingVariability	VIKINGv20170715	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCDR1	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCDR2	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCDR3	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCDR4	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCDR5	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20110816	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20110909	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20120126	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20121128	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20130304	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20130805	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20140428	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20140903	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20150309	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20151218	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20160311	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20160822	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20170109	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20170411	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20171101	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20180702	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20181120	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20191212	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20210708	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20230816	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcVariability	VMCv20240226	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcdeepVariability	VMCDEEPv20230713	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vmcdeepVariability	VMCDEEPv20240506	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vvvVariability	VVVDR5	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vvvVariability	VVVv20100531	Median J magnitude	real	4	mag	-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag	vvvxVariability	VVVXDR1	Median J magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.median;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmfID	ultravistaMergeLog, ultravistaRemeasMergeLog	ULTRAVISTADR4	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSDR1	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vhsMergeLog	VHSDR2	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vhsMergeLog	VHSDR3	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSDR4	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSDR5	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSDR6	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20120926	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vhsMergeLog	VHSv20130417	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vhsMergeLog	VHSv20140409	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20150108	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20160114	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20160507	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20170630	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20180419	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20201209	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20231101	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vhsMergeLog	VHSv20240731	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	videoMergeLog	VIDEODR2	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	videoMergeLog	VIDEODR3	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	videoMergeLog	VIDEODR4	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	videoMergeLog	VIDEODR5	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	videoMergeLog	VIDEOv20100513	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	videoMergeLog	VIDEOv20111208	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vikingMergeLog	VIKINGDR2	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vikingMergeLog	VIKINGDR3	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vikingMergeLog	VIKINGDR4	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vikingMergeLog	VIKINGv20110714	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vikingMergeLog	VIKINGv20111019	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vikingMergeLog	VIKINGv20130417	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vikingMergeLog	VIKINGv20140402	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vikingMergeLog	VIKINGv20150421	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vikingMergeLog	VIKINGv20151230	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vikingMergeLog	VIKINGv20160406	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vikingMergeLog	VIKINGv20161202	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vikingMergeLog	VIKINGv20170715	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20170124	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vmcMergeLog	VMCDR2	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vmcMergeLog	VMCDR3	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCDR4	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCDR5	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20110816	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vmcMergeLog	VMCv20110909	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vmcMergeLog	VMCv20120126	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vmcMergeLog	VMCv20121128	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vmcMergeLog	VMCv20130304	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vmcMergeLog	VMCv20130805	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vmcMergeLog	VMCv20140428	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20140903	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20150309	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20151218	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20160311	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20160822	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20170109	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20170411	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20171101	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20180702	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20181120	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20191212	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20210708	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20230816	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog	VMCv20240226	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcMergeLog, vmcSynopticMergeLog	VMCDR1	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vmcdeepMergeLog	VMCDEEPv20240506	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vmcdeepMergeLog, vmcdeepSynopticMergeLog	VMCDEEPv20230713	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vvvMergeLog	VVVDR2	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vvvMergeLog	VVVv20100531	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vvvMergeLog	VVVv20110718	the UID of the relevant J multiframe	bigint	8			obs.field
jmfID	vvvMergeLog, vvvPsfDaophotJKsMergeLog	VVVDR5	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmfID	vvvMergeLog, vvvSynopticMergeLog	VVVDR1	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field
jmfID	vvvxMergeLog	VVVXDR1	the UID of the relevant J multiframe	bigint	8			meta.id;obs.field;em.IR.J
jmh	vhsSourceRemeasurement	VHSDR1	Default colour J-H (using appropriate mags)	real	4	mag		PHOT_COLOR
jmh	videoSourceRemeasurement	VIDEOv20100513	Default colour J-H (using appropriate mags)	real	4	mag		PHOT_COLOR
jmh	vikingSourceRemeasurement	VIKINGv20110714	Default colour J-H (using appropriate mags)	real	4	mag		PHOT_COLOR
jmh	vikingSourceRemeasurement	VIKINGv20111019	Default colour J-H (using appropriate mags)	real	4	mag		PHOT_COLOR
jmh	vvvSourceRemeasurement	VVVv20100531	Default colour J-H (using appropriate mags)	real	4	mag		PHOT_COLOR
jmh	vvvSourceRemeasurement	VVVv20110718	Default colour J-H (using appropriate mags)	real	4	mag		PHOT_COLOR
jmhErr	vhsSourceRemeasurement	VHSDR1	Error on colour J-H	real	4	mag		stat.error
jmhErr	videoSourceRemeasurement	VIDEOv20100513	Error on colour J-H	real	4	mag		stat.error
jmhErr	vikingSourceRemeasurement	VIKINGv20110714	Error on colour J-H	real	4	mag		stat.error
jmhErr	vikingSourceRemeasurement	VIKINGv20111019	Error on colour J-H	real	4	mag		stat.error
jmhErr	vvvSourceRemeasurement	VVVv20100531	Error on colour J-H	real	4	mag		stat.error
jmhErr	vvvSourceRemeasurement	VVVv20110718	Error on colour J-H	real	4	mag		stat.error
jmhExt	ultravistaSource	ULTRAVISTADR4	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSDR1	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSDR2	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSDR3	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSDR4	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSDR5	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSDR6	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20120926	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20130417	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20140409	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20150108	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20160114	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20160507	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20170630	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20180419	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20201209	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20231101	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vhsSource	VHSv20240731	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	videoSource	VIDEODR2	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	videoSource	VIDEODR3	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	videoSource	VIDEODR4	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	videoSource	VIDEODR5	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	videoSource	VIDEOv20100513	Extended source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	videoSource	VIDEOv20111208	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGDR2	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGDR3	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGDR4	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20110714	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20111019	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20130417	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20140402	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20150421	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20151230	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20160406	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20161202	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingSource	VIKINGv20170715	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source colour J-H (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt	vvvSource	VVVv20100531	Extended source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	ultravistaSource	ULTRAVISTADR4	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSDR1	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSDR2	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSDR3	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSDR4	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSDR5	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSDR6	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20120926	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20130417	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20140409	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20150108	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20160114	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20160507	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20170630	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20180419	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20201209	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20231101	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vhsSource	VHSv20240731	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	videoSource	VIDEODR2	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	videoSource	VIDEODR3	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	videoSource	VIDEODR4	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	videoSource	VIDEODR5	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	videoSource	VIDEOv20100513	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	videoSource	VIDEOv20111208	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGDR2	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGDR3	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGDR4	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20110714	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20111019	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20130417	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20140402	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20150421	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20151230	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20160406	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20161202	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingSource	VIKINGv20170715	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr	vvvSource	VVVv20100531	Error on extended source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source colour calibrated flux H/J (using aperJkyNoAperCorr3)	real	4	jansky	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source colour calibrated flux H/J (using aperJkyNoAperCorr3)	real	4	jansky	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source colour calibrated flux H/J (using aperJkyNoAperCorr3)	real	4	jansky	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on extended source colour calibrated flux H/J	real	4	jansky	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on extended source colour calibrated flux H/J	real	4	jansky	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on extended source colour calibrated flux H/J	real	4	jansky	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source colour luptitudeJ-H (using aperLupNoAperCorr3)	real	4	lup	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source colour luptitudeJ-H (using aperLupNoAperCorr3)	real	4	lup	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source colour luptitudeJ-H (using aperLupNoAperCorr3)	real	4	lup	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on extended source colour luptitude J-H	real	4	lup	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on extended source colour luptitude J-H	real	4	lup	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on extended source colour luptitude J-H	real	4	lup	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	ultravistaSource	ULTRAVISTADR4	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSDR1	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSDR2	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSDR3	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSDR4	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSDR5	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSDR6	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20120926	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20130417	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20140409	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20150108	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20160114	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20160507	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20170630	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20180419	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20201209	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20231101	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vhsSource	VHSv20240731	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	videoSource	VIDEODR2	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	videoSource	VIDEODR3	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	videoSource	VIDEODR4	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	videoSource	VIDEODR5	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	videoSource	VIDEOv20100513	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	videoSource	VIDEOv20111208	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGDR2	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGDR3	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGDR4	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20110714	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20111019	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20130417	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20140402	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20150421	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20151230	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20160406	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20161202	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingSource	VIKINGv20170715	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvPsfDophotZYJHKsSource	VVVDR5	Point source colour J-H (using PsfMag)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvSource	VVVDR2	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvSource	VVVDR5	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvSource	VVVv20100531	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvSource	VVVv20110718	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvSource, vvvSynopticSource	VVVDR1	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt	vvvxSource	VVVXDR1	Point source colour J-H (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	ultravistaSource	ULTRAVISTADR4	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSDR1	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSDR2	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSDR3	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSDR4	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSDR5	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSDR6	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20120926	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20130417	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20140409	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20150108	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20160114	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20160507	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20170630	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20180419	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20201209	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20231101	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vhsSource	VHSv20240731	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	videoSource	VIDEODR2	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	videoSource	VIDEODR3	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	videoSource	VIDEODR4	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	videoSource	VIDEODR5	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	videoSource	VIDEOv20100513	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	videoSource	VIDEOv20111208	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGDR2	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGDR3	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGDR4	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20110714	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20111019	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20130417	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20140402	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20150421	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20151230	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20160406	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20161202	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingSource	VIKINGv20170715	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvPsfDophotZYJHKsSource	VVVDR5	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvSource	VVVDR2	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvSource	VVVDR5	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvSource	VVVv20100531	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvSource	VVVv20110718	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvSource, vvvSynopticSource	VVVDR1	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr	vvvxSource	VVVXDR1	Error on point source colour J-H	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.H
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source colour calibrated flux H/J (using aperJky3)	real	4	jansky	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source colour calibrated flux H/J (using aperJky3)	real	4	jansky	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source colour calibrated flux H/J (using aperJky3)	real	4	jansky	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on point source colour calibrated flux H/J	real	4	jansky	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on point source colour calibrated flux H/J	real	4	jansky	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on point source colour calibrated flux H/J	real	4	jansky	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source colour luptitude J-H (using aperLup3)	real	4	lup	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source colour luptitude J-H (using aperLup3)	real	4	lup	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source colour luptitude J-H (using aperLup3)	real	4	lup	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on point source colour luptitude J-H	real	4	lup	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on point source colour luptitude J-H	real	4	lup	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on point source colour luptitude J-H	real	4	lup	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jminCadence	ultravistaMapLcVariability	ULTRAVISTADR4	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	ultravistaVariability	ULTRAVISTADR4	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	videoVariability	VIDEODR2	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	videoVariability	VIDEODR3	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	videoVariability	VIDEODR4	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	videoVariability	VIDEODR5	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	videoVariability	VIDEOv20100513	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	videoVariability	VIDEOv20111208	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGDR2	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGDR3	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGDR4	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20110714	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20111019	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20130417	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20140402	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20150421	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20151230	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20160406	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20161202	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vikingVariability	VIKINGv20170715	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCDR1	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCDR2	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCDR3	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCDR4	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCDR5	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20110816	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20110909	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20120126	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20121128	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20130304	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20130805	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20140428	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20140903	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20150309	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20151218	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20160311	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20160822	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20170109	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20170411	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20171101	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20180702	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20181120	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20191212	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20210708	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20230816	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcVariability	VMCv20240226	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcdeepVariability	VMCDEEPv20230713	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vmcdeepVariability	VMCDEEPv20240506	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vvvVariability	VVVDR5	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vvvVariability	VVVv20100531	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence	vvvxVariability	VVVXDR1	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jMinMag	ultravistaMapLcVariability	ULTRAVISTADR4	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	ultravistaVariability	ULTRAVISTADR4	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	videoVariability	VIDEODR2	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	videoVariability	VIDEODR3	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9	phot.mag;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	videoVariability	VIDEODR4	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	videoVariability	VIDEODR5	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	videoVariability	VIDEOv20100513	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	videoVariability	VIDEOv20111208	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGDR2	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGDR3	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGDR4	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20110714	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20111019	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20130417	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20140402	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20150421	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20151230	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20160406	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20161202	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vikingVariability	VIKINGv20170715	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCDR1	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCDR2	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCDR3	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCDR4	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCDR5	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20110816	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20110909	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20120126	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20121128	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20130304	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20130805	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20140428	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20140903	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20150309	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20151218	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20160311	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20160822	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20170109	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20170411	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20171101	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20180702	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20181120	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20191212	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20210708	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20230816	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcVariability	VMCv20240226	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcdeepVariability	VMCDEEPv20230713	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vmcdeepVariability	VMCDEEPv20240506	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vvvVariability	VVVDR5	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vvvVariability	VVVv20100531	Minimum magnitude in J band, of good detections	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag	vvvxVariability	VVVXDR1	Minimum magnitude in J band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.J;stat.min
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMjd	ultravistaSource	ULTRAVISTADR4	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	ultravistaSourceRemeasurement	ULTRAVISTADR4	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vhsSource	VHSDR5	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vhsSource	VHSDR6	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vhsSource	VHSv20160114	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vhsSource	VHSv20160507	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vhsSource	VHSv20170630	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vhsSource	VHSv20180419	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vhsSource	VHSv20201209	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vhsSource	VHSv20231101	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vhsSource	VHSv20240731	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vikingSource	VIKINGv20151230	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vikingSource	VIKINGv20160406	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vikingSource	VIKINGv20161202	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vikingSource	VIKINGv20170715	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcSource	VMCDR5	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20151218	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcSource	VMCv20160311	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcSource	VMCv20160822	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcSource	VMCv20170109	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcSource	VMCv20170411	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcSource	VMCv20171101	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20180702	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20181120	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20191212	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20210708	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20230816	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource	VMCv20240226	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcSource, vmcSynopticSource	VMCDR4	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vmcdeepSource	VMCDEEPv20230713	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcdeepSource	VMCDEEPv20240506	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vmcdeepSynopticSource	VMCDEEPv20230713	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;meta.main
jMjd	vmcdeepSynopticSource	VMCDEEPv20240506	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;meta.main
jMjd	vvvSource	VVVDR5	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jMjd	vvvSynopticSource	VVVDR1	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vvvSynopticSource	VVVDR2	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch
jMjd	vvvxSource	VVVXDR1	Modified Julian Day in J band	float	8	days	-0.9999995e9	time.epoch;em.IR.J
jmks	vmcSourceRemeasurement	VMCv20110816	Default colour J-Ks (using appropriate mags)	real	4	mag		PHOT_COLOR
jmks	vmcSourceRemeasurement	VMCv20110909	Default colour J-Ks (using appropriate mags)	real	4	mag		PHOT_COLOR
jmksErr	vmcSourceRemeasurement	VMCv20110816	Error on colour J-Ks	real	4	mag		stat.error
jmksErr	vmcSourceRemeasurement	VMCv20110909	Error on colour J-Ks	real	4	mag		stat.error
jmksExt	vhsSource	VHSDR1	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSDR2	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSDR3	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSDR4	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSDR5	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSDR6	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20120926	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20130417	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20140409	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20150108	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20160114	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20160507	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20170630	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20180419	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20201209	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20231101	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vhsSource	VHSv20240731	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCDR1	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCDR2	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCDR3	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCDR4	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCDR5	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20110816	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20110909	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20120126	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20121128	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20130304	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20130805	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20140428	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20140903	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20150309	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20151218	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20160311	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20160822	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20170109	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20170411	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20171101	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20180702	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20181120	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20191212	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20210708	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20230816	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcSource	VMCv20240226	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcdeepSource	VMCDEEPv20230713	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt	vmcdeepSource	VMCDEEPv20240506	Extended source colour J-Ks (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSDR1	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSDR2	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSDR3	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSDR4	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSDR5	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSDR6	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20120926	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20130417	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20140409	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20150108	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20160114	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20160507	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20170630	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20180419	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20201209	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20231101	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vhsSource	VHSv20240731	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCDR1	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCDR2	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCDR3	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCDR4	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCDR5	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20110816	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20110909	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20120126	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20121128	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20130304	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20130805	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20140428	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20140903	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20150309	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20151218	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20160311	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20160822	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20170109	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20170411	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20171101	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20180702	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20181120	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20191212	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20210708	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20230816	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcSource	VMCv20240226	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcdeepSource	VMCDEEPv20230713	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr	vmcdeepSource	VMCDEEPv20240506	Error on extended source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSDR1	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSDR2	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSDR3	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSDR4	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSDR5	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSDR6	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20120926	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20130417	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20140409	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20150108	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20160114	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20160507	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20170630	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20180419	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20201209	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20231101	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vhsSource	VHSv20240731	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCDR2	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCDR3	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCDR4	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCDR5	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20110816	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20110909	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20120126	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20121128	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20130304	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20130805	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20140428	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20140903	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20150309	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20151218	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20160311	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20160822	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20170109	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20170411	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20171101	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20180702	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20181120	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20191212	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20210708	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20230816	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource	VMCv20240226	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcSource, vmcSynopticSource	VMCDR1	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	PHOT_COLOR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcdeepSource	VMCDEEPv20240506	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Point source colour J-Ks (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt	vvvPsfDaophotJKsSource	VVVDR5	Point source colour J-Ks (using PsfMag)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntCor	vvvPsfDaophotJKsSource	VVVDR5	Point source colour J-Ks (using PsfMagCor)	real	4	mag	-0.9999995e9	phot.color
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSDR1	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSDR2	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSDR3	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSDR4	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSDR5	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSDR6	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20120926	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20130417	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20140409	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20150108	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20160114	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20160507	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20170630	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20180419	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20201209	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20231101	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vhsSource	VHSv20240731	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCDR2	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCDR3	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCDR4	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCDR5	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20110816	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20110909	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20120126	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20121128	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20130304	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20130805	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20140428	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20140903	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20150309	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20151218	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20160311	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20160822	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20170109	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20170411	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20171101	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20180702	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20181120	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20191212	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20210708	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20230816	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource	VMCv20240226	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcSource, vmcSynopticSource	VMCDR1	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcdeepSource	VMCDEEPv20240506	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr	vvvPsfDaophotJKsSource	VVVDR5	Error on point source colour J-Ks	real	4	mag	-0.9999995e9	stat.error
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPsf	vmcPsfCatalogue	VMCDR4	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20150309	J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20151218	J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20160311	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20160822	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20170109	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20170411	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfCatalogue	VMCv20171101	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCDR5	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCv20180702	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCv20181120	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCv20191212	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCv20210708	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCv20230816	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf	vmcPsfSource	VMCv20240226	J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCDR4	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20150309	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20151218	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20160311	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20160822	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20170109	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20170411	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfCatalogue	VMCv20171101	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCDR5	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCv20180702	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCv20181120	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCv20191212	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCv20210708	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCv20230816	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jmksPsfErr	vmcPsfSource	VMCv20240226	Error on J-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.J;em.IR.K
jndof	ultravistaMapLcVariability	ULTRAVISTADR4	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	ultravistaVariability	ULTRAVISTADR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	videoVariability	VIDEODR2	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	videoVariability	VIDEODR3	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	videoVariability	VIDEODR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	videoVariability	VIDEODR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	videoVariability	VIDEOv20100513	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	videoVariability	VIDEOv20111208	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGDR2	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGDR3	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGDR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20110714	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20111019	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20130417	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20140402	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20150421	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20151230	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20160406	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20161202	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vikingVariability	VIKINGv20170715	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCDR1	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCDR2	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCDR3	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCDR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCDR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20110816	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20110909	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20120126	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20121128	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20130304	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20130805	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20140428	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20140903	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20150309	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20151218	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20160311	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20160822	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20170109	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20170411	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20171101	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20180702	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20181120	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20191212	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20210708	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20230816	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcVariability	VMCv20240226	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcdeepVariability	VMCDEEPv20230713	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vmcdeepVariability	VMCDEEPv20240506	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vvvVariability	VVVDR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vvvVariability	VVVv20100531	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof	vvvxVariability	VVVXDR1	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jnDofAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	videoVarFrameSetInfo	VIDEODR2	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	videoVarFrameSetInfo	VIDEODR3	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	videoVarFrameSetInfo	VIDEODR4	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	videoVarFrameSetInfo	VIDEODR5	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	videoVarFrameSetInfo	VIDEOv20100513	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	videoVarFrameSetInfo	VIDEOv20111208	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGDR2	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGDR3	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGDR4	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20110714	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20111019	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20130417	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20140402	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20150421	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20151230	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20160406	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20161202	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vikingVarFrameSetInfo	VIKINGv20170715	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCDR1	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCDR2	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCDR3	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCDR4	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCDR5	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20110816	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20110909	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20120126	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20121128	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20130304	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20130805	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20140428	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20140903	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20150309	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20151218	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20160311	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20160822	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20170109	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20170411	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20171101	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20180702	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20181120	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20191212	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20210708	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20230816	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcVarFrameSetInfo	VMCv20240226	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vvvVarFrameSetInfo	VVVDR5	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vvvVarFrameSetInfo	VVVv20100531	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst	vvvxVarFrameSetInfo	VVVXDR1	Number of degrees of freedom of astrometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	videoVarFrameSetInfo	VIDEODR2	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	videoVarFrameSetInfo	VIDEODR3	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	videoVarFrameSetInfo	VIDEODR4	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	videoVarFrameSetInfo	VIDEODR5	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	videoVarFrameSetInfo	VIDEOv20100513	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	videoVarFrameSetInfo	VIDEOv20111208	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGDR2	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGDR3	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGDR4	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20110714	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20111019	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20130417	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20140402	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20150421	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20151230	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20160406	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20161202	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vikingVarFrameSetInfo	VIKINGv20170715	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCDR1	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCDR2	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCDR3	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCDR4	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCDR5	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20110816	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20110909	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20120126	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20121128	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20130304	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20130805	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20140428	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20140903	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20150309	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20151218	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20160311	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20160822	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20170109	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20170411	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20171101	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20180702	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20181120	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20191212	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20210708	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20230816	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcVarFrameSetInfo	VMCv20240226	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vvvVarFrameSetInfo	VVVDR5	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vvvVarFrameSetInfo	VVVv20100531	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht	vvvxVarFrameSetInfo	VVVXDR1	Number of degrees of freedom of photometric fit in J band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.J
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jNepochs	vmcCepheidVariables	VMCDR4	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20160311	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20160822	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20170109	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20170411	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20171101	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20180702	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20181120	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20191212	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20210708	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20230816	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jNepochs	vmcCepheidVariables	VMCv20240226	Number of J mag epochs {catalogue TType keyword: o_Jmag}	int	4		-99999999	meta.number;em.IR.J
jnFlaggedObs	ultravistaVariability	ULTRAVISTADR4	Number of detections in J band flagged as potentially spurious by ultravistaDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	videoVariability	VIDEODR2	Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	videoVariability	VIDEODR3	Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	videoVariability	VIDEODR4	Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	videoVariability	VIDEODR5	Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	videoVariability	VIDEOv20100513	Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	videoVariability	VIDEOv20111208	Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGDR2	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGDR3	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGDR4	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20110714	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20111019	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20130417	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20140402	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20150421	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20151230	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20160406	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20161202	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vikingVariability	VIKINGv20170715	Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCDR1	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCDR2	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCDR3	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCDR4	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCDR5	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20110816	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20110909	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20120126	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20121128	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20130304	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20130805	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20140428	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20140903	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20150309	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20151218	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20160311	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20160822	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20170109	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20170411	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20171101	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20180702	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20181120	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20191212	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20210708	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20230816	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcVariability	VMCv20240226	Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcdeepVariability	VMCDEEPv20230713	Number of detections in J band flagged as potentially spurious by vmcdeepDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vmcdeepVariability	VMCDEEPv20240506	Number of detections in J band flagged as potentially spurious by vmcdeepDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vvvVariability	VVVDR5	Number of detections in J band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vvvVariability	VVVv20100531	Number of detections in J band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs	vvvxVariability	VVVXDR1	Number of detections in J band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	ultravistaVariability	ULTRAVISTADR4	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	videoVariability	VIDEODR2	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	videoVariability	VIDEODR3	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	videoVariability	VIDEODR4	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	videoVariability	VIDEODR5	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	videoVariability	VIDEOv20100513	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	videoVariability	VIDEOv20111208	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGDR2	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGDR3	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGDR4	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20110714	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20111019	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20130417	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20140402	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20150421	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20151230	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20160406	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20161202	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vikingVariability	VIKINGv20170715	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCDR1	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCDR2	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCDR3	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCDR4	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCDR5	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20110816	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20110909	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20120126	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20121128	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20130304	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20130805	Number of good detections in J band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20140428	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20140903	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20150309	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20151218	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20160311	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20160822	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20170109	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20170411	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20171101	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20180702	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20181120	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20191212	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20210708	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20230816	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcVariability	VMCv20240226	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcdeepVariability	VMCDEEPv20230713	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vmcdeepVariability	VMCDEEPv20240506	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vvvVariability	VVVDR5	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vvvVariability	VVVv20100531	Number of good detections in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs	vvvxVariability	VVVXDR1	Number of good detections in J band	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jNgt3sig	ultravistaMapLcVariability	ULTRAVISTADR4	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	ultravistaVariability	ULTRAVISTADR4	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	videoVariability	VIDEODR2	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	videoVariability	VIDEODR3	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	videoVariability	VIDEODR4	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	videoVariability	VIDEODR5	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	videoVariability	VIDEOv20100513	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	videoVariability	VIDEOv20111208	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGDR2	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGDR3	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGDR4	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20110714	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20111019	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20130417	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20140402	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20150421	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20151230	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20160406	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20161202	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vikingVariability	VIKINGv20170715	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCDR1	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCDR2	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCDR3	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCDR4	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCDR5	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20110816	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20110909	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20120126	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20121128	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20130304	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20130805	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20140428	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20140903	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20150309	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20151218	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20160311	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20160822	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20170109	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20170411	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20171101	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20180702	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20181120	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20191212	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20210708	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20230816	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcVariability	VMCv20240226	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcdeepVariability	VMCDEEPv20230713	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vmcdeepVariability	VMCDEEPv20240506	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vvvVariability	VVVDR5	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vvvVariability	VVVv20100531	Number of good detections in J-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig	vvvxVariability	VVVXDR1	Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms)	smallint	2		-9999	meta.number;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jnMissingObs	ultravistaVariability	ULTRAVISTADR4	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	videoVariability	VIDEODR2	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	videoVariability	VIDEODR3	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	videoVariability	VIDEODR4	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	videoVariability	VIDEODR5	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	videoVariability	VIDEOv20100513	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	videoVariability	VIDEOv20111208	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGDR2	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGDR3	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGDR4	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20110714	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20111019	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20130417	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20140402	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20150421	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20151230	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20160406	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20161202	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vikingVariability	VIKINGv20170715	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCDR1	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCDR2	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCDR3	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCDR4	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCDR5	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20110816	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20110909	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20120126	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20121128	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20130304	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20130805	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20140428	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20140903	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20150309	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20151218	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20160311	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20160822	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20170109	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20170411	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20171101	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20180702	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20181120	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20191212	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20210708	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20230816	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcVariability	VMCv20240226	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcdeepVariability	VMCDEEPv20230713	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vmcdeepVariability	VMCDEEPv20240506	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vvvVariability	VVVDR5	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vvvVariability	VVVv20100531	Number of J band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs	vvvxVariability	VVVXDR1	Number of J band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnNegFlagObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of flagged negative measurements in J band by ultravistaMapRemeasurement.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnNegObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of unflagged negative measurements J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnPosFlagObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of flagged positive measurements in J band by ultravistaMapRemeasurement.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnPosObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of unflagged positive measurements in J band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
joinCriterion	RequiredNeighbours	SHARKSv20210222	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	SHARKSv20210421	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	ULTRAVISTADR4	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSDR1	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSDR2	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSDR3	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSDR4	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSDR5	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSDR6	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20120926	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20130417	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20150108	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20160114	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20160507	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20170630	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20180419	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20201209	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20231101	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VHSv20240731	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIDEODR2	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIDEODR3	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIDEODR4	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIDEODR5	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIDEOv20100513	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIDEOv20111208	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGDR2	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGDR3	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGDR4	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20110714	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20111019	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20130417	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20150421	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20151230	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20160406	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20161202	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VIKINGv20170715	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCDEEPv20230713	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCDEEPv20240506	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCDR1	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCDR3	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCDR4	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCDR5	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20110816	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20110909	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20120126	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20121128	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20130304	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20130805	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20140428	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20140903	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20150309	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20151218	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20160311	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20160822	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20170109	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20170411	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20171101	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20180702	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20181120	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20191212	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20210708	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20230816	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VMCv20240226	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VSAQC	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VVVDR1	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VVVDR2	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VVVDR5	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VVVXDR1	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VVVv20100531	the join criterion (search radius for matches)	real	4	degrees		??
joinCriterion	RequiredNeighbours	VVVv20110718	the join criterion (search radius for matches)	real	4	degrees		??
JON	grs_ngpSource, grs_ranSource, grs_sgpSource	TWODFGRS	Eyeball classification, value > 0 only for galaxies brighter than about 18th mag: 0 = noise; 1 = S0; 2 = elliptical; 3 = spiral; 4 = irregular; 5 = undetermined galaxy; 6 = star; 7 = star + star merger; 8 = galaxy + star merger; 9 = galaxy + galaxy merger	int	4
jPA	ultravistaSource	ULTRAVISTADR4	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	ultravistaSourceRemeasurement	ULTRAVISTADR4	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vhsSource	VHSDR2	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vhsSource	VHSDR3	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSDR4	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSDR5	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSDR6	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20120926	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vhsSource	VHSv20130417	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vhsSource	VHSv20140409	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20150108	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20160114	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20160507	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20170630	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20180419	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20201209	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20231101	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource	VHSv20240731	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vhsSource, vhsSourceRemeasurement	VHSDR1	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	videoSource	VIDEODR2	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	videoSource	VIDEODR3	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	videoSource	VIDEODR4	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	videoSource	VIDEODR5	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	videoSource	VIDEOv20111208	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	videoSource, videoSourceRemeasurement	VIDEOv20100513	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingSource	VIKINGDR2	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingSource	VIKINGDR3	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingSource	VIKINGDR4	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vikingSource	VIKINGv20111019	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingSource	VIKINGv20130417	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingSource	VIKINGv20140402	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingSource	VIKINGv20150421	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vikingSource	VIKINGv20151230	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vikingSource	VIKINGv20160406	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vikingSource	VIKINGv20161202	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vikingSource	VIKINGv20170715	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCDR2	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCDR3	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCDR4	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCDR5	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20110909	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCv20120126	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCv20121128	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCv20130304	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCv20130805	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource	VMCv20140428	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20140903	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20150309	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20151218	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20160311	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20160822	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20170109	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20170411	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20171101	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20180702	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20181120	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20191212	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20210708	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20230816	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource	VMCv20240226	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcSource, vmcSourceRemeasurement	VMCv20110816	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcSource, vmcSynopticSource	VMCDR1	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vmcdeepSource	VMCDEEPv20240506	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vvvSource	VVVDR2	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vvvSource	VVVDR5	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPA	vvvSource	VVVv20110718	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vvvSource, vvvSourceRemeasurement	VVVv20100531	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vvvSource, vvvSynopticSource	VVVDR1	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng
jPA	vvvxSource	VVVXDR1	ellipse fit celestial orientation in J	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.J
jPetroJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J calibrated flux (Petrosian)	real	4	jansky	-0.9999995e9	phot.flux
jPetroJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source J calibrated flux (Petrosian)	real	4	jansky	-0.9999995e9	stat.error
jPetroLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J luptitude (Petrosian)	real	4	lup	-0.9999995e9	phot.lup
jPetroLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source J luptitude (Petrosian)	real	4	lup	-0.9999995e9	stat.error
jPetroMag	ultravistaSource	ULTRAVISTADR4	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source J magnitude (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vhsSource	VHSDR1	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vhsSource	VHSDR2	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vhsSource	VHSDR3	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSDR4	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSDR5	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSDR6	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20120926	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vhsSource	VHSv20130417	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vhsSource	VHSv20140409	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20150108	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20160114	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20160507	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20170630	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20180419	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20201209	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20231101	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vhsSource	VHSv20240731	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	videoSource	VIDEODR2	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	videoSource	VIDEODR3	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	videoSource	VIDEODR4	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	videoSource	VIDEODR5	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	videoSource	VIDEOv20100513	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	videoSource	VIDEOv20111208	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vikingSource	VIKINGDR2	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vikingSource	VIKINGDR3	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vikingSource	VIKINGDR4	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vikingSource	VIKINGv20110714	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vikingSource	VIKINGv20111019	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vikingSource	VIKINGv20130417	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vikingSource	VIKINGv20140402	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vikingSource	VIKINGv20150421	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vikingSource	VIKINGv20151230	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vikingSource	VIKINGv20160406	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vikingSource	VIKINGv20161202	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vikingSource	VIKINGv20170715	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCDR1	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vmcSource	VMCDR2	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCDR3	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCDR4	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCDR5	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20110816	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vmcSource	VMCv20110909	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vmcSource	VMCv20120126	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vmcSource	VMCv20121128	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vmcSource	VMCv20130304	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
jPetroMag	vmcSource	VMCv20130805	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20140428	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20140903	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20150309	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20151218	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20160311	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20160822	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20170109	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20170411	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20171101	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20180702	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20181120	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20191212	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20210708	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20230816	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcSource	VMCv20240226	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcdeepSource	VMCDEEPv20230713	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMag	vmcdeepSource	VMCDEEPv20240506	Extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPetroMagErr	ultravistaSource	ULTRAVISTADR4	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source J magnitude (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vhsSource	VHSDR1	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vhsSource	VHSDR2	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vhsSource	VHSDR3	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPetroMagErr	vhsSource	VHSDR4	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	vhsSource	VHSDR5	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSDR6	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20120926	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vhsSource	VHSv20130417	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vhsSource	VHSv20140409	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPetroMagErr	vhsSource	VHSv20150108	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	vhsSource	VHSv20160114	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20160507	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20170630	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20180419	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20201209	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20231101	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vhsSource	VHSv20240731	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	videoSource	VIDEODR2	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	videoSource	VIDEODR3	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	videoSource	VIDEODR4	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	videoSource	VIDEODR5	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	videoSource	VIDEOv20100513	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	videoSource	VIDEOv20111208	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGDR2	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGDR3	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGDR4	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPetroMagErr	vikingSource	VIKINGv20110714	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGv20111019	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGv20130417	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGv20140402	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vikingSource	VIKINGv20150421	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	vikingSource	VIKINGv20151230	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vikingSource	VIKINGv20160406	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vikingSource	VIKINGv20161202	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vikingSource	VIKINGv20170715	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCDR1	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCDR2	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCDR3	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	vmcSource	VMCDR4	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCDR5	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20110816	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCv20110909	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCv20120126	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCv20121128	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCv20130304	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCv20130805	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
jPetroMagErr	vmcSource	VMCv20140428	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPetroMagErr	vmcSource	VMCv20140903	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	vmcSource	VMCv20150309	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPetroMagErr	vmcSource	VMCv20151218	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20160311	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20160822	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20170109	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20170411	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20171101	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20180702	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20181120	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20191212	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20210708	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20230816	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcSource	VMCv20240226	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcdeepSource	VMCDEEPv20230713	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPetroMagErr	vmcdeepSource	VMCDEEPv20240506	Error in extended source J mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jppErrBits	ultravistaSource	ULTRAVISTADR4	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
jppErrBits	ultravistaSourceRemeasurement	ULTRAVISTADR4	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSDR1	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSDR2	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSDR3	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSDR4	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSDR5	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSDR6	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20120926	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20130417	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20140409	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20150108	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20160114	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20160507	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20170630	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20180419	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20201209	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20231101	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSource	VHSv20240731	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vhsSourceRemeasurement	VHSDR1	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	videoSource	VIDEODR2	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	videoSource	VIDEODR3	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	videoSource	VIDEODR4	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
jppErrBits	videoSource	VIDEODR5	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
jppErrBits	videoSource	VIDEOv20111208	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	videoSource, videoSourceRemeasurement	VIDEOv20100513	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vikingSource	VIKINGDR2	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGDR3	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGDR4	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20110714	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20111019	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20130417	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20140402	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20150421	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20151230	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20160406	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20161202	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSource	VIKINGv20170715	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingSourceRemeasurement	VIKINGv20110714	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vikingSourceRemeasurement	VIKINGv20111019	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCDR2	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCDR3	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCDR4	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCDR5	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20110816	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20110909	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20120126	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20121128	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20130304	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20130805	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20140428	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20140903	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20150309	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20151218	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20160311	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20160822	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20170109	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20170411	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20171101	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20180702	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20181120	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20191212	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20210708	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20230816	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource	VMCv20240226	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSource, vmcSynopticSource	VMCDR1	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcSourceRemeasurement	VMCv20110816	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vmcSourceRemeasurement	VMCv20110909	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vmcdeepSource	VMCDEEPv20240506	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vvvSource	VVVDR1	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vvvSource	VVVDR2	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
jppErrBits	vvvSource	VVVv20110718	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vvvSource, vvvSourceRemeasurement	VVVv20100531	additional WFAU post-processing error bits in J	int	4		0	meta.code
jppErrBits	vvvSynopticSource	VVVDR1	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vvvSynopticSource	VVVDR2	additional WFAU post-processing error bits in J	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits	vvvxSource	VVVXDR1	additional WFAU post-processing error bits in J	int	4		0	meta.code;em.IR.J
jprobVar	ultravistaMapLcVariability	ULTRAVISTADR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	ultravistaVariability	ULTRAVISTADR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	videoVariability	VIDEODR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	videoVariability	VIDEODR3	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	videoVariability	VIDEODR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	videoVariability	VIDEODR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	videoVariability	VIDEOv20100513	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	videoVariability	VIDEOv20111208	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGDR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGDR3	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGDR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20110714	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20111019	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20130417	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20140402	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20150421	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20151230	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20160406	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20161202	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vikingVariability	VIKINGv20170715	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCDR1	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCDR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCDR3	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCDR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCDR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20110816	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20110909	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20120126	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20121128	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20130304	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20130805	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20140428	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20140903	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20150309	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20151218	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20160311	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20160822	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20170109	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20170411	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20171101	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20180702	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20181120	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20191212	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20210708	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20230816	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcVariability	VMCv20240226	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcdeepVariability	VMCDEEPv20230713	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vmcdeepVariability	VMCDEEPv20240506	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vvvVariability	VVVDR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vvvVariability	VVVv20100531	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar	vvvxVariability	VVVXDR1	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jPsfMag	vhsSource	VHSDR1	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vhsSource	VHSDR2	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vhsSource	VHSDR3	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSDR4	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSDR5	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSDR6	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20120926	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vhsSource	VHSv20130417	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vhsSource	VHSv20140409	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20150108	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20160114	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20160507	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20170630	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20180419	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20201209	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20231101	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vhsSource	VHSv20240731	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vikingSource	VIKINGDR2	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vikingSource	VIKINGDR3	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vikingSource	VIKINGDR4	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vikingSource	VIKINGv20110714	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vikingSource	VIKINGv20111019	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vikingSource	VIKINGv20130417	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vikingSource	VIKINGv20140402	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vikingSource	VIKINGv20150421	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vikingSource	VIKINGv20151230	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vikingSource	VIKINGv20160406	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vikingSource	VIKINGv20161202	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vikingSource	VIKINGv20170715	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCDR3	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCDR4	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20121128	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param
jPsfMag	vmcPsfCatalogue	VMCv20140428	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20140903	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20150309	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20151218	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20160311	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20160822	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20170109	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20170411	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfCatalogue	VMCv20171101	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfDetections	VMCv20180702	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfDetections	VMCv20181120	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J
jPsfMag	vmcPsfSource	VMCDR5	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcPsfSource	VMCv20180702	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcPsfSource	VMCv20181120	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcPsfSource	VMCv20191212	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcPsfSource	VMCv20210708	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcPsfSource	VMCv20230816	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcPsfSource	VMCv20240226	3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag	vmcSource	VMCDR1	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vmcSource	VMCDR2	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCDR3	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCDR4	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCDR5	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20110816	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vmcSource	VMCv20110909	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vmcSource	VMCv20120126	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vmcSource	VMCv20121128	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vmcSource	VMCv20130304	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag
jPsfMag	vmcSource	VMCv20130805	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20140428	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20140903	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20150309	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20151218	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20160311	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20160822	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20170109	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20170411	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20171101	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20180702	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20181120	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20191212	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20210708	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20230816	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcSource	VMCv20240226	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcdeepSource	VMCDEEPv20230713	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vmcdeepSource	VMCDEEPv20240506	Point source profile-fitted J mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jPsfMag	vvvPsfDaophotJKsSource	VVVDR5	PSF magnitude in J band {catalogue TType keyword: J}	real	4	mag	-0.9999995e9	instr.det.psf;phot.mag;em.IR.J;meta.main
jPsfMag	vvvPsfDophotZYJHKsSource	VVVDR5	Mean PSF magnitude in J band {catalogue TType keyword: mag_J}	real	4	mag	-0.9999995e9	instr.det.psf;phot.mag;em.IR.J;meta.main
jPsfMagCor	vvvPsfDaophotJKsSource	VVVDR5	Reddening corrected PSF magnitude in J band (J_0) {catalogue TType keyword: cJ}	real	4	mag	-0.9999995e9	phys.absorption;instr.det.psf;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSDR1	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vhsSource	VHSDR2	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vhsSource	VHSDR3	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPsfMagErr	vhsSource	VHSDR4	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPsfMagErr	vhsSource	VHSDR5	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSDR6	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20120926	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vhsSource	VHSv20130417	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vhsSource	VHSv20140409	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPsfMagErr	vhsSource	VHSv20150108	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPsfMagErr	vhsSource	VHSv20160114	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20160507	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20170630	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20180419	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20201209	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20231101	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vhsSource	VHSv20240731	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGDR2	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGDR3	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGDR4	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPsfMagErr	vikingSource	VIKINGv20110714	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGv20111019	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGv20130417	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGv20140402	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vikingSource	VIKINGv20150421	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPsfMagErr	vikingSource	VIKINGv20151230	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vikingSource	VIKINGv20160406	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vikingSource	VIKINGv20161202	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vikingSource	VIKINGv20170715	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCDR3	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCDR4	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20121128	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcPsfCatalogue	VMCv20140428	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20140903	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20150309	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20151218	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20160311	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20160822	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20170109	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20170411	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfCatalogue	VMCv20171101	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfDetections	VMCv20181120	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcPsfDetections, vmcPsfSource	VMCv20180702	PSF error J filter {catalogue TType keyword: J_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCDR1	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCDR2	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCDR3	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPsfMagErr	vmcSource	VMCDR4	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCDR5	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20110816	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCv20110909	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCv20120126	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCv20121128	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCv20130304	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCv20130805	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error
jPsfMagErr	vmcSource	VMCv20140428	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jPsfMagErr	vmcSource	VMCv20140903	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPsfMagErr	vmcSource	VMCv20150309	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jPsfMagErr	vmcSource	VMCv20151218	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20160311	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20160822	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20170109	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20170411	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20171101	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20180702	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20181120	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20191212	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20210708	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20230816	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcSource	VMCv20240226	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcdeepSource	VMCDEEPv20230713	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vmcdeepSource	VMCDEEPv20240506	Error in point source profile-fitted J mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jPsfMagErr	vvvPsfDaophotJKsSource	VVVDR5	Error on PSF magnitude in J band {catalogue TType keyword: J_err}	real	4	mag	-0.9999995e9	stat.error;instr.det.psf;phot.mag;em.IR.J
jPsfMagErr	vvvPsfDophotZYJHKsSource	VVVDR5	Error on mean PSF magnitude in J band {catalogue TType keyword: er_J}	real	4	mag	-0.9999995e9	stat.error;instr.det.psf;em.IR.J
Jsep	vvvProperMotionCatalogue	VVVDR5	Sky distance between VVV DR4 J detection and the projected source position at the J observation epoch taking the pipeline proper motion into account. {catalogue TType keyword: Jsep}	real	4	arcsec	-999999500.0
jSeqNum	ultravistaSource	ULTRAVISTADR4	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSDR1	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vhsSource	VHSDR2	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vhsSource	VHSDR3	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSDR4	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSDR5	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSDR6	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20120926	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vhsSource	VHSv20130417	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vhsSource	VHSv20140409	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20150108	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20160114	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20160507	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20170630	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20180419	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vhsSource	VHSv20201209	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vhsSource	VHSv20231101	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vhsSource	VHSv20240731	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vhsSourceRemeasurement	VHSDR1	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	videoSource	VIDEODR2	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	videoSource	VIDEODR3	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	videoSource	VIDEODR4	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	videoSource	VIDEODR5	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	videoSource	VIDEOv20100513	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	videoSource	VIDEOv20111208	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	videoSourceRemeasurement	VIDEOv20100513	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vikingSource	VIKINGDR2	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vikingSource	VIKINGDR3	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vikingSource	VIKINGDR4	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vikingSource	VIKINGv20110714	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vikingSource	VIKINGv20111019	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vikingSource	VIKINGv20130417	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vikingSource	VIKINGv20140402	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vikingSource	VIKINGv20150421	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vikingSource	VIKINGv20151230	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vikingSource	VIKINGv20160406	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vikingSource	VIKINGv20161202	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vikingSource	VIKINGv20170715	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vikingSourceRemeasurement	VIKINGv20110714	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vikingSourceRemeasurement	VIKINGv20111019	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vmcSource	VMCDR2	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vmcSource	VMCDR3	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCDR4	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCDR5	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vmcSource	VMCv20110816	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vmcSource	VMCv20110909	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vmcSource	VMCv20120126	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vmcSource	VMCv20121128	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vmcSource	VMCv20130304	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vmcSource	VMCv20130805	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vmcSource	VMCv20140428	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20140903	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20150309	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20151218	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20160311	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20160822	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20170109	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20170411	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20171101	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20180702	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20181120	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vmcSource	VMCv20191212	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vmcSource	VMCv20210708	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vmcSource	VMCv20230816	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vmcSource	VMCv20240226	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vmcSource, vmcSynopticSource	VMCDR1	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vmcSourceRemeasurement	VMCv20110816	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vmcSourceRemeasurement	VMCv20110909	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vmcdeepSource	VMCDEEPv20240506	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSeqNum	vvvSource	VVVDR2	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vvvSource	VVVDR5	the running number of the J detection	int	4		-99999999	meta.number;em.IR.J
jSeqNum	vvvSource	VVVv20100531	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vvvSource	VVVv20110718	the running number of the J detection	int	4		-99999999	meta.id
jSeqNum	vvvSource, vvvSynopticSource	VVVDR1	the running number of the J detection	int	4		-99999999	meta.number
jSeqNum	vvvSourceRemeasurement	VVVv20100531	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vvvSourceRemeasurement	VVVv20110718	the running number of the J remeasurement	int	4		-99999999	meta.id
jSeqNum	vvvxSource	VVVXDR1	the running number of the J detection	int	4		-99999999	meta.id;em.IR.J
jSerMag2D	vhsSource	VHSDR1	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vhsSource	VHSDR2	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vhsSource	VHSDR3	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSDR4	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSDR5	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSDR6	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20120926	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vhsSource	VHSv20130417	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vhsSource	VHSv20140409	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20150108	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20160114	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20160507	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20170630	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20180419	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20201209	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20231101	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vhsSource	VHSv20240731	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vikingSource	VIKINGDR2	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vikingSource	VIKINGDR3	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vikingSource	VIKINGDR4	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vikingSource	VIKINGv20110714	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vikingSource	VIKINGv20111019	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vikingSource	VIKINGv20130417	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vikingSource	VIKINGv20140402	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vikingSource	VIKINGv20150421	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vikingSource	VIKINGv20151230	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vikingSource	VIKINGv20160406	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vikingSource	VIKINGv20161202	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vikingSource	VIKINGv20170715	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCDR1	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vmcSource	VMCDR2	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCDR3	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCDR4	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCDR5	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20110816	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vmcSource	VMCv20110909	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vmcSource	VMCv20120126	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vmcSource	VMCv20121128	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vmcSource	VMCv20130304	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
jSerMag2D	vmcSource	VMCv20130805	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20140428	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20140903	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20150309	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20151218	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20160311	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20160822	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20170109	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20170411	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20171101	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20180702	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20181120	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20191212	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20210708	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20230816	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcSource	VMCv20240226	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcdeepSource	VMCDEEPv20230713	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2D	vmcdeepSource	VMCDEEPv20240506	Extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSDR1	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vhsSource	VHSDR2	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vhsSource	VHSDR3	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jSerMag2DErr	vhsSource	VHSDR4	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jSerMag2DErr	vhsSource	VHSDR5	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSDR6	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20120926	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vhsSource	VHSv20130417	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vhsSource	VHSv20140409	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jSerMag2DErr	vhsSource	VHSv20150108	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jSerMag2DErr	vhsSource	VHSv20160114	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20160507	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20170630	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20180419	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20201209	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20231101	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vhsSource	VHSv20240731	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGDR2	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGDR3	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGDR4	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jSerMag2DErr	vikingSource	VIKINGv20110714	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGv20111019	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGv20130417	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGv20140402	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vikingSource	VIKINGv20150421	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jSerMag2DErr	vikingSource	VIKINGv20151230	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vikingSource	VIKINGv20160406	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vikingSource	VIKINGv20161202	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vikingSource	VIKINGv20170715	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCDR1	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCDR2	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCDR3	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jSerMag2DErr	vmcSource	VMCDR4	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCDR5	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20110816	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCv20110909	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCv20120126	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCv20121128	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCv20130304	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCv20130805	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
jSerMag2DErr	vmcSource	VMCv20140428	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J
jSerMag2DErr	vmcSource	VMCv20140903	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jSerMag2DErr	vmcSource	VMCv20150309	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.J;phot.mag
jSerMag2DErr	vmcSource	VMCv20151218	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20160311	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20160822	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20170109	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20170411	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20171101	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20180702	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20181120	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20191212	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20210708	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20230816	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcSource	VMCv20240226	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcdeepSource	VMCDEEPv20230713	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSerMag2DErr	vmcdeepSource	VMCDEEPv20240506	Error in extended source J mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.J
jSharp	vmcPsfCatalogue	VMCDR3	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCDR4	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20121128	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param
jSharp	vmcPsfCatalogue	VMCv20140428	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20140903	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20150309	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20151218	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20160311	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20160822	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20170109	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20170411	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfCatalogue	VMCv20171101	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfDetections	VMCv20181120	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jSharp	vmcPsfDetections, vmcPsfSource	VMCv20180702	PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.J
jskewness	ultravistaMapLcVariability	ULTRAVISTADR4	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	ultravistaVariability	ULTRAVISTADR4	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	videoVariability	VIDEODR2	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	videoVariability	VIDEODR3	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	videoVariability	VIDEODR4	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	videoVariability	VIDEODR5	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	videoVariability	VIDEOv20100513	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	videoVariability	VIDEOv20111208	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGDR2	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGDR3	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGDR4	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20110714	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20111019	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20130417	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20140402	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20150421	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20151230	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20160406	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20161202	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vikingVariability	VIKINGv20170715	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCDR1	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCDR2	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCDR3	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCDR4	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCDR5	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20110816	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20110909	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20120126	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20121128	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20130304	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20130805	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20140428	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20140903	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20150309	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20151218	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20160311	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20160822	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20170109	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20170411	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20171101	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20180702	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20181120	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20191212	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20210708	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20230816	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcVariability	VMCv20240226	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcdeepVariability	VMCDEEPv20230713	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vmcdeepVariability	VMCDEEPv20240506	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vvvVariability	VVVDR5	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vvvVariability	VVVv20100531	Skewness in J band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness	vvvxVariability	VVVXDR1	Skewness in J band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jtotalPeriod	ultravistaMapLcVariability	ULTRAVISTADR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	ultravistaVariability	ULTRAVISTADR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	videoVariability	VIDEODR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	videoVariability	VIDEODR3	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	videoVariability	VIDEODR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	videoVariability	VIDEODR5	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	videoVariability	VIDEOv20100513	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	videoVariability	VIDEOv20111208	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGDR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGDR3	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGDR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20110714	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20111019	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20130417	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20140402	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20150421	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20151230	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20160406	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20161202	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vikingVariability	VIKINGv20170715	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCDR1	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCDR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCDR3	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCDR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCDR5	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20110816	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20110909	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20120126	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20121128	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20130304	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20130805	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20140428	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration;em.IR.J
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20140903	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20150309	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20151218	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20160311	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20160822	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20170109	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20170411	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20171101	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20180702	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20181120	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20191212	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20210708	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20230816	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcVariability	VMCv20240226	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcdeepVariability	VMCDEEPv20230713	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vmcdeepVariability	VMCDEEPv20240506	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vvvVariability	VVVDR5	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vvvVariability	VVVv20100531	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod	vvvxVariability	VVVXDR1	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
Julian	denisDR3Source	DENIS	Julian day for DENIS observation	real	4	JD
julianDayNum	Multiframe	SHARKSv20210222	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	SHARKSv20210421	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	ULTRAVISTADR4	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSDR1	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSDR2	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSDR3	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSDR4	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSDR5	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSDR6	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20120926	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20130417	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20140409	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20150108	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20160114	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20160507	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20170630	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20180419	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20201209	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20231101	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VHSv20240731	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIDEODR2	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIDEODR3	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIDEODR4	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIDEODR5	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIDEOv20100513	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIDEOv20111208	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGDR2	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGDR3	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGDR4	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20110714	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20111019	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20130417	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20140402	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20150421	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20151230	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20160406	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20161202	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VIKINGv20170715	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDEEPv20230713	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDEEPv20240506	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDR1	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDR2	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDR3	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDR4	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCDR5	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20110816	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20110909	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20120126	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20121128	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20130304	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20130805	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20140428	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20140903	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20150309	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20151218	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20160311	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20160822	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20170109	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20170411	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20171101	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20180702	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20181120	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20191212	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20210708	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20230816	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VMCv20240226	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VVVDR1	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VVVDR2	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VVVDR5	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VVVXDR1	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VVVv20100531	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	Multiframe	VVVv20110718	The Julian Day number of the VISTA night	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	SHARKSv20210222	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	SHARKSv20210421	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	ULTRAVISTADR4	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSDR1	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSDR2	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSDR3	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSDR4	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSDR5	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSDR6	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20120926	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20130417	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20140409	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20150108	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20160114	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20160507	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20170630	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20180419	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20201209	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20231101	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VHSv20240731	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIDEODR2	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIDEODR3	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIDEODR4	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIDEODR5	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIDEOv20100513	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIDEOv20111208	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGDR2	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGDR3	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGDR4	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20110714	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20111019	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20130417	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20140402	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20150421	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20151230	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20160406	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20161202	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VIKINGv20170715	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDEEPv20230713	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDEEPv20240506	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDR1	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDR2	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDR3	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDR4	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCDR5	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20110816	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20110909	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20120126	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20121128	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20130304	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20130805	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20140428	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20140903	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20150309	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20151218	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20160311	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20160822	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20170109	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20170411	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20171101	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20180702	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20181120	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20191212	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20210708	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20230816	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VMCv20240226	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VVVDR1	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VVVDR2	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VVVDR5	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VVVXDR1	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VVVv20100531	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	MultiframeDetector	VVVv20110718	The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS}	int	4	Julian days	-99999999	time.epoch
julianDayNum	sharksMultiframe, sharksMultiframeDetector, ultravistaMultiframe, ultravistaMultiframeDetector, vhsMultiframe, vhsMultiframeDetector, videoMultiframe, videoMultiframeDetector, vikingMultiframe, vikingMultiframeDetector, vmcMultiframe, vmcMultiframeDetector, vvvMultiframe, vvvMultiframeDetector	VSAQC	The Julian Day number of the VISTA night	int	4	Julian days		time.epoch
jVarClass	ultravistaMapLcVariability	ULTRAVISTADR4	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	ultravistaVariability	ULTRAVISTADR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	videoVariability	VIDEODR2	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	videoVariability	VIDEODR3	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	videoVariability	VIDEODR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	videoVariability	VIDEODR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	videoVariability	VIDEOv20100513	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	videoVariability	VIDEOv20111208	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGDR2	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGDR3	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGDR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20110714	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20111019	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20130417	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20140402	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20150421	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20151230	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20160406	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20161202	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vikingVariability	VIKINGv20170715	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCDR1	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCDR2	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCDR3	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCDR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCDR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20110816	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20110909	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20120126	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20121128	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20130304	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20130805	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20140428	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20140903	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20150309	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20151218	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20160311	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20160822	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20170109	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20170411	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20171101	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20180702	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20181120	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20191212	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20210708	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20230816	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcVariability	VMCv20240226	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcdeepVariability	VMCDEEPv20230713	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vmcdeepVariability	VMCDEEPv20240506	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vvvVariability	VVVDR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vvvVariability	VVVv20100531	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass	vvvxVariability	VVVXDR1	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.J
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jXi	ultravistaSource	ULTRAVISTADR4	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSDR1	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSDR2	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSDR3	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSDR4	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSDR5	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSDR6	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20120926	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20130417	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20140409	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20150108	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20160114	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20160507	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20170630	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20180419	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20201209	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20231101	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vhsSource	VHSv20240731	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	videoSource	VIDEODR2	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	videoSource	VIDEODR3	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	videoSource	VIDEODR4	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	videoSource	VIDEODR5	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	videoSource	VIDEOv20100513	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	videoSource	VIDEOv20111208	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGDR2	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGDR3	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGDR4	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20110714	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20111019	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20130417	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20140402	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20150421	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20151230	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20160406	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20161202	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vikingSource	VIKINGv20170715	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCDR2	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCDR3	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCDR4	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCDR5	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20110816	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20110909	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20120126	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20121128	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20130304	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20130805	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20140428	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20140903	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20150309	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20151218	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20160311	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20160822	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20170109	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20170411	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20171101	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20180702	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20181120	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20191212	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20210708	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20230816	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource	VMCv20240226	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcSource, vmcSynopticSource	VMCDR1	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcdeepSource	VMCDEEPv20240506	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vvvSource	VVVDR2	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vvvSource	VVVDR5	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vvvSource	VVVv20100531	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vvvSource	VVVv20110718	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vvvSource, vvvSynopticSource	VVVDR1	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi	vvvxSource	VVVXDR1	Offset of J detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.J
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.