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Glossary of VSA attributes
This Glossary alphabetically lists all attributes used in the VSAv20250328 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20250328 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

Y
Name	Schema Table	Database	Description	Type	Length	Unit	Default Value	Unified Content Descriptor
y	allwise_sc	WISE	Unit sphere position y value	float	8
y	combo17CDFSSource	COMBO17	y-coordinate on image cdfs_r.fit	real	4	pix
y	sharksDetection	SHARKSv20210222	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	sharksDetection	SHARKSv20210421	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	smashdr2_source	SMASH	Y-coordinate for this source in the original chip (1-indexed)	real	4
y	ultravistaDetection, ultravistaMapRemeasurement	ULTRAVISTADR4	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSDR2	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSDR3	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSDR4	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSDR5	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSDR6	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSDR7	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20120926	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20130417	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20140409	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20150108	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20160114	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20160507	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20170630	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20180419	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20201209	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection	VHSv20231101	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vhsDetection, vhsListRemeasurement	VHSDR1	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoDetection	VIDEODR2	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoDetection	VIDEODR3	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoDetection	VIDEODR4	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoDetection	VIDEODR5	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoDetection	VIDEOv20100513	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoDetection	VIDEOv20111208	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	videoListRemeasurement	VIDEOv20100513	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGDR2	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGDR3	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGDR4	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20111019	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20130417	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20140402	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20150421	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20151230	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20160406	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20161202	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection	VIKINGv20170715	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingDetection, vikingListRemeasurement	VIKINGv20110714	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingMapRemeasurement	VIKINGZYSELJv20160909	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vikingMapRemeasurement	VIKINGZYSELJv20170124	Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCDR1	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCDR2	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCDR3	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCDR4	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCDR5	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20110909	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20120126	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20121128	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20130304	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20130805	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20140428	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20140903	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20150309	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20151218	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20160311	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20160822	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20170109	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20170411	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20171101	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20180702	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20181120	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20191212	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20210708	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20230816	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20240226	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection	VMCv20250130	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcDetection, vmcListRemeasurement	VMCv20110816	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcdeepDetection	VMCDEEPv20230713	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vmcdeepDetection	VMCDEEPv20240506	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vvvDetection	VVVDR1	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vvvDetection	VVVDR2	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vvvDetection	VVVDR4	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y	vvvDetection, vvvListRemeasurement	VVVv20100531	Y coordinate of detection {catalogue TType keyword: Y_coordinate} Intensity-weighted isophotal centre-of-gravity in Y.	real	4	pixels		pos.cartesian.y;instr.plate
y_1AperMag1	vvvSource	VVVDR5	Point source Y_1 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
y_1AperMag1Err	vvvSource	VVVDR5	Error in point source Y_1 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
y_1AperMag3	vvvSource	VVVDR5	Default point source Y_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.NIR
y_1AperMag3Err	vvvSource	VVVDR5	Error in default point source Y_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
y_1AperMag4	vvvSource	VVVDR5	Point source Y_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
y_1AperMag4Err	vvvSource	VVVDR5	Error in point source Y_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
y_1AverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) Y_1	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
y_1Class	vvvSource	VVVDR5	discrete image classification flag in Y_1	smallint	2		-9999	src.class;em.IR.NIR
y_1ClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in Y_1	real	4		-0.9999995e9	stat;em.IR.NIR
y_1Ell	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in Y_1	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
y_1eNum	vvvMergeLog	VVVDR5	the extension number of this Y_1 frame	tinyint	1			meta.number;em.IR.NIR
y_1eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR4	the extension number of this 1st epoch Y frame	tinyint	1			meta.number;em.IR.NIR
y_1eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the extension number of this 1st epoch Y frame	tinyint	1			meta.number;em.IR.NIR
y_1ErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in Y_1	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
y_1Eta	vvvSource	VVVDR5	Offset of Y_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
y_1Gausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in Y_1	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
y_1mfID	vvvMergeLog	VVVDR5	the UID of the relevant Y_1 multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
y_1mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR4	the UID of the relevant 1st epoch Y tile multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
y_1mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the UID of the relevant 1st epoch Y tile multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
y_1Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR4	the MJD of the 1st epoch Y tile multiframe	float	8			time;em.IR.NIR
y_1Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the MJD of the 1st epoch Y tile multiframe	float	8			time;em.IR.NIR
y_1Mjd	vvvSource	VVVDR5	Modified Julian Day in Y_1 band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
y_1PA	vvvSource	VVVDR5	ellipse fit celestial orientation in Y_1	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
y_1ppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in Y_1	int	4		0	meta.code;em.IR.NIR
y_1SeqNum	vvvSource	VVVDR5	the running number of the Y_1 detection	int	4		-99999999	meta.number;em.IR.NIR
y_1Xi	vvvSource	VVVDR5	Offset of Y_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
y_2AperMag1	vvvSource	VVVDR5	Point source Y_2 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
y_2AperMag1Err	vvvSource	VVVDR5	Error in point source Y_2 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
y_2AperMag3	vvvSource	VVVDR5	Default point source Y_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.NIR
y_2AperMag3Err	vvvSource	VVVDR5	Error in default point source Y_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
y_2AperMag4	vvvSource	VVVDR5	Point source Y_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
y_2AperMag4Err	vvvSource	VVVDR5	Error in point source Y_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
y_2AverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) Y_2	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
y_2Class	vvvSource	VVVDR5	discrete image classification flag in Y_2	smallint	2		-9999	src.class;em.IR.NIR
y_2ClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in Y_2	real	4		-0.9999995e9	stat;em.IR.NIR
y_2Ell	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in Y_2	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
y_2eNum	vvvMergeLog	VVVDR5	the extension number of this Y_2 frame	tinyint	1			meta.number;em.IR.NIR
y_2eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR4	the extension number of this 2nd epoch Y frame	tinyint	1			meta.number;em.IR.NIR
y_2eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the extension number of this 2nd epoch Y frame	tinyint	1			meta.number;em.IR.NIR
y_2ErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in Y_2	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
y_2Eta	vvvSource	VVVDR5	Offset of Y_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
y_2Gausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in Y_2	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
y_2mfID	vvvMergeLog	VVVDR5	the UID of the relevant Y_2 multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
y_2mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR4	the UID of the relevant 2nd epoch Y tile multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
y_2mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the UID of the relevant 2nd epoch Y tile multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
y_2Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR4	the MJD of the 2nd epoch Y tile multiframe	float	8			time;em.IR.NIR
y_2Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the MJD of the 2nd epoch Y tile multiframe	float	8			time;em.IR.NIR
y_2Mjd	vvvSource	VVVDR5	Modified Julian Day in Y_2 band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
y_2PA	vvvSource	VVVDR5	ellipse fit celestial orientation in Y_2	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
y_2ppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in Y_2	int	4		0	meta.code;em.IR.NIR
y_2SeqNum	vvvSource	VVVDR5	the running number of the Y_2 detection	int	4		-99999999	meta.number;em.IR.NIR
y_2Xi	vvvSource	VVVDR5	Offset of Y_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
Y_BJ	grs_ngpSource, grs_ranSource, grs_sgpSource	TWODFGRS	Plate y_bj in 8 micron pixels	real	4
y_coadd	twomass_xsc	TWOMASS	y (in-scan) position (coadd coord.).	real	4	arcsec		pos.cartesian;instr.det
Y_IMAGE	mgcDetection	MGC	Object y position	real	4	pixel
Y_OFF	mgcGalaxyStruct	MGC	Y offset of Galaxy Centre	real	4		99.99
Y_OFFm	mgcGalaxyStruct	MGC	Y offset error (-)	real	4		99.99
Y_OFFp	mgcGalaxyStruct	MGC	Y offset error (+)	real	4		99.99
Y_R	spectra	SIXDF	y position of object from R frame	int	4
Y_V	spectra	SIXDF	y position of object from V frame	int	4
yAmpl	vmcCepheidVariables	VMCDR4	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20160311	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20160822	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20170109	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20170411	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20171101	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20180702	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20181120	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20191212	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20210708	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20230816	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20240226	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmpl	vmcCepheidVariables	VMCv20250130	Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCDR4	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20160311	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20160822	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20170109	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20170411	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20171101	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20180702	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20181120	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20191212	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20210708	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20230816	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20240226	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAmplErr	vmcCepheidVariables	VMCv20250130	Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.NIR
yAperJky3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source Y aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
yAperJky3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source Y aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
yAperJky3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source Y aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
yAperJky3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source Y (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source Y (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source Y (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Y aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJky4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Y aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJky4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Y aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJky4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Y (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Y (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Y (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Y aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJky6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Y aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJky6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Y aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJky6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Y (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Y (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJky6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Y (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
yAperJkyNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source Y (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
yAperJkyNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source Y (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
yAperJkyNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source Y (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
yAperJkyNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJkyNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJkyNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJkyNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJkyNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperJkyNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
yAperLup3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source Y aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
yAperLup3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source Y aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
yAperLup3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source Y aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
yAperLup3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source Y (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source Y (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source Y (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Y aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLup4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Y aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLup4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Y aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLup4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Y (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Y (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Y (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Y aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLup6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Y aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLup6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Y aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLup6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Y (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Y (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLup6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Y (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
yAperLupNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source Y (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
yAperLupNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source Y (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
yAperLupNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source Y (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
yAperLupNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLupNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLupNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLupNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLupNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperLupNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
yAperMag1	vmcSynopticSource	VMCDR1	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vmcSynopticSource	VMCDR2	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCDR3	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCDR4	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCDR5	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20110816	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vmcSynopticSource	VMCv20110909	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vmcSynopticSource	VMCv20120126	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vmcSynopticSource	VMCv20121128	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vmcSynopticSource	VMCv20130304	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vmcSynopticSource	VMCv20130805	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20140428	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20140903	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20150309	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20151218	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20160311	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20160822	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20170109	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20170411	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20171101	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20180702	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20181120	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20191212	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20210708	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20230816	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20240226	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vmcSynopticSource	VMCv20250130	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vvvSource	VVVDR1	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vvvSource	VVVDR4	Point source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vvvSource	VVVDR5	Point source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1	vvvSource	VVVv20100531	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vvvSource	VVVv20110718	Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag1	vvvSource, vvvSynopticSource	VVVDR2	Extended source Y aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCDR1	Error in extended source Y mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCDR2	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCDR3	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag1Err	vmcSynopticSource	VMCDR4	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCDR5	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20110816	Error in extended source Y mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCv20110909	Error in extended source Y mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCv20120126	Error in extended source Y mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCv20121128	Error in extended source Y mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCv20130304	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCv20130805	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vmcSynopticSource	VMCv20140428	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20140903	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag1Err	vmcSynopticSource	VMCv20150309	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag1Err	vmcSynopticSource	VMCv20151218	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20160311	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20160822	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20170109	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20170411	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20171101	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20180702	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20181120	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20191212	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20210708	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20230816	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20240226	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vmcSynopticSource	VMCv20250130	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vvvSource	VVVDR1	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vvvSource	VVVDR4	Error in point source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vvvSource	VVVDR5	Error in point source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag1Err	vvvSource	VVVv20100531	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vvvSource	VVVv20110718	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag1Err	vvvSource, vvvSynopticSource	VVVDR2	Error in extended source Y mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2	vmcSynopticSource	VMCDR1	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vmcSynopticSource	VMCDR2	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCDR3	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCDR4	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCDR5	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20110816	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vmcSynopticSource	VMCv20110909	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vmcSynopticSource	VMCv20120126	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vmcSynopticSource	VMCv20121128	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vmcSynopticSource	VMCv20130304	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vmcSynopticSource	VMCv20130805	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20140428	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20140903	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20150309	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20151218	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20160311	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20160822	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20170109	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20170411	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20171101	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20180702	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20181120	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20191212	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20210708	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20230816	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20240226	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vmcSynopticSource	VMCv20250130	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vvvSynopticSource	VVVDR1	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag2	vvvSynopticSource	VVVDR2	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2	vvvSynopticSource	VVVDR4	Extended source Y aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCDR1	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCDR2	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCDR3	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag2Err	vmcSynopticSource	VMCDR4	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCDR5	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20110816	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCv20110909	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCv20120126	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCv20121128	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCv20130304	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCv20130805	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vmcSynopticSource	VMCv20140428	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20140903	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag2Err	vmcSynopticSource	VMCv20150309	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag2Err	vmcSynopticSource	VMCv20151218	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20160311	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20160822	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20170109	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20170411	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20171101	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20180702	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20181120	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20191212	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20210708	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20230816	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20240226	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vmcSynopticSource	VMCv20250130	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag2Err	vvvSynopticSource	VVVDR1	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vvvSynopticSource	VVVDR2	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag2Err	vvvSynopticSource	VVVDR4	Error in extended source Y mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3	ultravistaSource	ULTRAVISTADR4	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source Y aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vhsSource	VHSDR1	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vhsSource	VHSDR2	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vhsSource	VHSDR3	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSDR4	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSDR5	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSDR6	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSDR7	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20120926	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vhsSource	VHSv20130417	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vhsSource	VHSv20140409	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20150108	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20160114	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20160507	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20170630	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20180419	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20201209	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vhsSource	VHSv20231101	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	videoSource	VIDEODR2	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	videoSource	VIDEODR3	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	videoSource	VIDEODR4	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	videoSource	VIDEODR5	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	videoSource	VIDEOv20100513	Default point/extended source Y mag, no aperture correction applied If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	videoSource	VIDEOv20111208	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingSource	VIKINGDR2	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingSource	VIKINGDR3	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingSource	VIKINGDR4	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingSource	VIKINGv20110714	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingSource	VIKINGv20111019	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingSource	VIKINGv20130417	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingSource	VIKINGv20140402	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingSource	VIKINGv20150421	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingSource	VIKINGv20151230	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingSource	VIKINGv20160406	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingSource	VIKINGv20161202	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingSource	VIKINGv20170715	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source Y aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source Y aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCDR1	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCDR2	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCDR3	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCDR4	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCDR5	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20110816	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCv20110909	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCv20120126	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCv20121128	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCv20130304	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSource	VMCv20130805	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20140428	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20140903	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20150309	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20151218	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20160311	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20160822	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20170109	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20170411	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20171101	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20180702	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20181120	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20191212	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20210708	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20230816	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20240226	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSource	VMCv20250130	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCDR1	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSynopticSource	VMCDR2	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCDR3	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCDR4	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCDR5	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20110816	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSynopticSource	VMCv20110909	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSynopticSource	VMCv20120126	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSynopticSource	VMCv20121128	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSynopticSource	VMCv20130304	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vmcSynopticSource	VMCv20130805	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20140428	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20140903	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20150309	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20151218	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20160311	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20160822	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20170109	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20170411	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20171101	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20180702	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20181120	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20191212	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20210708	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20230816	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20240226	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vmcSynopticSource	VMCv20250130	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vvvSource	VVVDR1	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vvvSource	VVVDR2	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vvvSource	VVVDR4	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vvvSource	VVVDR5	Default point source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vvvSource	VVVv20100531	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vvvSource	VVVv20110718	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vvvSynopticSource	VVVDR1	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag3	vvvSynopticSource	VVVDR2	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vvvSynopticSource	VVVDR4	Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag3	vvvVivaCatalogue	VVVDR4	Y magnitude using aperture corrected mag (2.0 arcsec aperture diameter, from VVVDR4 1st epoch ZY contemporaneous OB) {catalogue TType keyword: yAperMag3}	real	4	mag	-9.999995e8
yAperMag3	vvvVivaCatalogue	VVVDR5	Y magnitude using aperture corrected mag (2.0 arcsec aperture diameter, from VVVDR4 1st epoch ZY contemporaneous OB) {catalogue TType keyword: yAperMag3}	real	4	mag	-9.999995e8
yAperMag3Err	ultravistaSource	ULTRAVISTADR4	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source Y (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vhsSource	VHSDR1	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vhsSource	VHSDR2	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vhsSource	VHSDR3	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag3Err	vhsSource	VHSDR4	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	vhsSource	VHSDR5	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSDR6	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSDR7	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20120926	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vhsSource	VHSv20130417	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vhsSource	VHSv20140409	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20150108	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	vhsSource	VHSv20160114	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20160507	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20170630	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20180419	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20201209	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vhsSource	VHSv20231101	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	videoSource	VIDEODR2	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	videoSource	VIDEODR3	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	videoSource	VIDEODR4	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	videoSource	VIDEODR5	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	videoSource	VIDEOv20100513	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	videoSource	VIDEOv20111208	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGDR2	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGDR3	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGDR4	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag3Err	vikingSource	VIKINGv20110714	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGv20111019	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGv20130417	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGv20140402	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingSource	VIKINGv20150421	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	vikingSource	VIKINGv20151230	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vikingSource	VIKINGv20160406	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vikingSource	VIKINGv20161202	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vikingSource	VIKINGv20170715	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source Y (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source Y (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCDR2	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCDR3	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	vmcSource	VMCDR4	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCDR5	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20110816	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCv20110909	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCv20120126	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCv20121128	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCv20130304	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCv20130805	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vmcSource	VMCv20140428	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20140903	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	vmcSource	VMCv20150309	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag3Err	vmcSource	VMCv20151218	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20160311	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20160822	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20170109	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20170411	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20171101	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20180702	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20181120	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20191212	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20210708	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20230816	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20240226	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource	VMCv20250130	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vmcSource, vmcSynopticSource	VMCDR1	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vvvSource	VVVDR2	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vvvSource	VVVDR4	Error in default point source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vvvSource	VVVDR5	Error in default point source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag3Err	vvvSource	VVVv20100531	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vvvSource	VVVv20110718	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vvvSource, vvvSynopticSource	VVVDR1	Error in default point/extended source Y mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag3Err	vvvVivaCatalogue	VVVDR4	Error in default point source Y mag, from VVVDR4 {catalogue TType keyword: yAperMag3Err}	real	4	mag	-9.999995e8
yAperMag3Err	vvvVivaCatalogue	VVVDR5	Error in default point source Y mag, from VVVDR4 {catalogue TType keyword: yAperMag3Err}	real	4	mag	-9.999995e8
yAperMag4	ultravistaSource	ULTRAVISTADR4	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Y aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vhsSource	VHSDR1	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vhsSource	VHSDR2	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vhsSource	VHSDR3	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSDR4	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSDR5	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSDR6	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSDR7	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20120926	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vhsSource	VHSv20130417	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vhsSource	VHSv20140409	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20150108	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20160114	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20160507	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20170630	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20180419	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20201209	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vhsSource	VHSv20231101	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	videoSource	VIDEODR2	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	videoSource	VIDEODR3	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	videoSource	VIDEODR4	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	videoSource	VIDEODR5	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	videoSource	VIDEOv20100513	Extended source Y mag, no aperture correction applied	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	videoSource	VIDEOv20111208	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingSource	VIKINGDR2	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingSource	VIKINGDR3	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingSource	VIKINGDR4	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingSource	VIKINGv20110714	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingSource	VIKINGv20111019	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingSource	VIKINGv20130417	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingSource	VIKINGv20140402	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingSource	VIKINGv20150421	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingSource	VIKINGv20151230	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingSource	VIKINGv20160406	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingSource	VIKINGv20161202	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingSource	VIKINGv20170715	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Y aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Y aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCDR1	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCDR2	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCDR3	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCDR4	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCDR5	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20110816	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCv20110909	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCv20120126	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCv20121128	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCv20130304	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSource	VMCv20130805	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20140428	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20140903	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20150309	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20151218	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20160311	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20160822	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20170109	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20170411	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20171101	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20180702	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20181120	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20191212	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20210708	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20230816	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20240226	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSource	VMCv20250130	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCDR1	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSynopticSource	VMCDR2	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCDR3	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCDR4	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCDR5	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20110816	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSynopticSource	VMCv20110909	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSynopticSource	VMCv20120126	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSynopticSource	VMCv20121128	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSynopticSource	VMCv20130304	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vmcSynopticSource	VMCv20130805	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20140428	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20140903	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20150309	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20151218	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20160311	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20160822	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20170109	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20170411	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20171101	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20180702	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20181120	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20191212	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20210708	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20230816	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20240226	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vmcSynopticSource	VMCv20250130	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vvvSource	VVVDR2	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vvvSource	VVVDR4	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vvvSource	VVVDR5	Point source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag4	vvvSource	VVVv20100531	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vvvSource	VVVv20110718	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4	vvvSource, vvvSynopticSource	VVVDR1	Extended source Y aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag4Err	ultravistaSource	ULTRAVISTADR4	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Y (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vhsSource	VHSDR1	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vhsSource	VHSDR2	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vhsSource	VHSDR3	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag4Err	vhsSource	VHSDR4	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vhsSource	VHSDR5	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSDR6	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSDR7	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20120926	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vhsSource	VHSv20130417	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vhsSource	VHSv20140409	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20150108	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vhsSource	VHSv20160114	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20160507	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20170630	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20180419	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20201209	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vhsSource	VHSv20231101	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	videoSource	VIDEODR2	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	videoSource	VIDEODR3	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	videoSource	VIDEODR4	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	videoSource	VIDEODR5	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	videoSource	VIDEOv20100513	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	videoSource	VIDEOv20111208	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGDR2	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGDR3	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGDR4	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag4Err	vikingSource	VIKINGv20110714	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGv20111019	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGv20130417	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGv20140402	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingSource	VIKINGv20150421	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vikingSource	VIKINGv20151230	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vikingSource	VIKINGv20160406	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vikingSource	VIKINGv20161202	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vikingSource	VIKINGv20170715	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Y (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Y (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCDR1	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCDR2	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCDR3	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vmcSource	VMCDR4	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCDR5	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20110816	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCv20110909	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCv20120126	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCv20121128	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCv20130304	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCv20130805	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSource	VMCv20140428	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20140903	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vmcSource	VMCv20150309	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vmcSource	VMCv20151218	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20160311	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20160822	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20170109	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20170411	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20171101	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20180702	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20181120	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20191212	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20210708	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20230816	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20240226	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSource	VMCv20250130	Error in point/extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCDR1	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCDR2	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCDR3	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vmcSynopticSource	VMCDR4	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCDR5	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20110816	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCv20110909	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCv20120126	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCv20121128	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCv20130304	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCv20130805	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vmcSynopticSource	VMCv20140428	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20140903	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vmcSynopticSource	VMCv20150309	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag4Err	vmcSynopticSource	VMCv20151218	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20160311	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20160822	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20170109	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20170411	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20171101	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20180702	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20181120	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20191212	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20210708	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20230816	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20240226	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vmcSynopticSource	VMCv20250130	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vvvSource	VVVDR2	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vvvSource	VVVDR4	Error in point source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vvvSource	VVVDR5	Error in point source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag4Err	vvvSource	VVVv20100531	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vvvSource	VVVv20110718	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag4Err	vvvSource, vvvSynopticSource	VVVDR1	Error in extended source Y mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5	vmcSynopticSource	VMCDR1	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vmcSynopticSource	VMCDR2	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCDR3	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCDR4	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCDR5	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20110816	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vmcSynopticSource	VMCv20110909	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vmcSynopticSource	VMCv20120126	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vmcSynopticSource	VMCv20121128	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vmcSynopticSource	VMCv20130304	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vmcSynopticSource	VMCv20130805	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20140428	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20140903	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20150309	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20151218	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20160311	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20160822	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20170109	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20170411	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20171101	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20180702	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20181120	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20191212	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20210708	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20230816	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20240226	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vmcSynopticSource	VMCv20250130	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vvvSynopticSource	VVVDR1	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag5	vvvSynopticSource	VVVDR2	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5	vvvSynopticSource	VVVDR4	Extended source Y aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCDR1	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCDR2	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCDR3	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag5Err	vmcSynopticSource	VMCDR4	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCDR5	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20110816	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCv20110909	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCv20120126	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCv20121128	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCv20130304	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCv20130805	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vmcSynopticSource	VMCv20140428	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20140903	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag5Err	vmcSynopticSource	VMCv20150309	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag5Err	vmcSynopticSource	VMCv20151218	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20160311	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20160822	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20170109	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20170411	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20171101	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20180702	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20181120	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20191212	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20210708	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20230816	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20240226	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vmcSynopticSource	VMCv20250130	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag5Err	vvvSynopticSource	VVVDR1	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vvvSynopticSource	VVVDR2	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag5Err	vvvSynopticSource	VVVDR4	Error in extended source Y mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6	ultravistaSource	ULTRAVISTADR4	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Y aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vhsSource	VHSDR1	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vhsSource	VHSDR2	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vhsSource	VHSDR3	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSDR4	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSDR5	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSDR6	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSDR7	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20120926	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vhsSource	VHSv20130417	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vhsSource	VHSv20140409	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20150108	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20160114	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20160507	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20170630	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20180419	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20201209	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vhsSource	VHSv20231101	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	videoSource	VIDEODR2	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	videoSource	VIDEODR3	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	videoSource	VIDEODR4	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	videoSource	VIDEODR5	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	videoSource	VIDEOv20100513	Extended source Y mag, no aperture correction applied	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	videoSource	VIDEOv20111208	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingSource	VIKINGDR2	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingSource	VIKINGDR3	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingSource	VIKINGDR4	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingSource	VIKINGv20110714	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingSource	VIKINGv20111019	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingSource	VIKINGv20130417	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingSource	VIKINGv20140402	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingSource	VIKINGv20150421	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingSource	VIKINGv20151230	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingSource	VIKINGv20160406	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingSource	VIKINGv20161202	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingSource	VIKINGv20170715	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Y aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Y aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCDR1	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCDR2	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCDR3	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCDR4	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCDR5	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20110816	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCv20110909	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCv20120126	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCv20121128	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCv20130304	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMag6	vmcSource	VMCv20130805	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20140428	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20140903	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20150309	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20151218	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20160311	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20160822	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20170109	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20170411	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20171101	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20180702	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20181120	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20191212	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20210708	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20230816	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20240226	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6	vmcSource	VMCv20250130	Point source Y aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMag6Err	ultravistaSource	ULTRAVISTADR4	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Y (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vhsSource	VHSDR1	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vhsSource	VHSDR2	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vhsSource	VHSDR3	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag6Err	vhsSource	VHSDR4	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	vhsSource	VHSDR5	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSDR6	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSDR7	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20120926	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vhsSource	VHSv20130417	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vhsSource	VHSv20140409	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20150108	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	vhsSource	VHSv20160114	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20160507	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20170630	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20180419	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20201209	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vhsSource	VHSv20231101	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	videoSource	VIDEODR2	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	videoSource	VIDEODR3	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	videoSource	VIDEODR4	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	videoSource	VIDEODR5	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	videoSource	VIDEOv20100513	Error in extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	videoSource	VIDEOv20111208	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGDR2	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGDR3	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGDR4	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag6Err	vikingSource	VIKINGv20110714	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGv20111019	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGv20130417	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGv20140402	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingSource	VIKINGv20150421	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	vikingSource	VIKINGv20151230	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vikingSource	VIKINGv20160406	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vikingSource	VIKINGv20161202	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vikingSource	VIKINGv20170715	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Y (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Y (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCDR1	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCDR2	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCDR3	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	vmcSource	VMCDR4	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCDR5	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20110816	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCv20110909	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCv20120126	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCv20121128	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCv20130304	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCv20130805	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
yAperMag6Err	vmcSource	VMCv20140428	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20140903	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	vmcSource	VMCv20150309	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yAperMag6Err	vmcSource	VMCv20151218	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20160311	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20160822	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20170109	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20170411	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20171101	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20180702	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20181120	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20191212	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20210708	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20230816	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20240226	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMag6Err	vmcSource	VMCv20250130	Error in point/extended source Y mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yAperMagNoAperCorr3	ultravistaSource	ULTRAVISTADR4	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source Y (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
yAperMagNoAperCorr3	vhsSource	VHSDR1	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vhsSource	VHSDR2	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vhsSource	VHSDR3	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSDR4	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSDR5	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSDR6	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSDR7	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20120926	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vhsSource	VHSv20130417	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vhsSource	VHSv20140409	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20150108	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20160114	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20160507	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20170630	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20180419	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20201209	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vhsSource	VHSv20231101	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	videoSource	VIDEODR2	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	videoSource	VIDEODR3	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	videoSource	VIDEODR4	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	videoSource	VIDEODR5	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	videoSource	VIDEOv20111208	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vikingSource	VIKINGDR2	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vikingSource	VIKINGDR3	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vikingSource	VIKINGDR4	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingSource	VIKINGv20110714	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vikingSource	VIKINGv20111019	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vikingSource	VIKINGv20130417	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vikingSource	VIKINGv20140402	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingSource	VIKINGv20150421	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingSource	VIKINGv20151230	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingSource	VIKINGv20160406	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingSource	VIKINGv20161202	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingSource	VIKINGv20170715	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source Y (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
yAperMagNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source Y (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
yAperMagNoAperCorr3	vmcSource	VMCDR1	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vmcSource	VMCDR2	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCDR3	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCDR4	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCDR5	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20110816	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vmcSource	VMCv20110909	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vmcSource	VMCv20120126	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vmcSource	VMCv20121128	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vmcSource	VMCv20130304	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr3	vmcSource	VMCv20130805	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20140428	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20140903	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20150309	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20151218	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20160311	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20160822	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20170109	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20170411	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20171101	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20180702	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20181120	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20191212	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20210708	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20230816	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20240226	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr3	vmcSource	VMCv20250130	Default extended source Y aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	ultravistaSource	ULTRAVISTADR4	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vhsSource	VHSDR1	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vhsSource	VHSDR2	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vhsSource	VHSDR3	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSDR4	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSDR5	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSDR6	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSDR7	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20120926	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vhsSource	VHSv20130417	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vhsSource	VHSv20140409	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20150108	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20160114	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20160507	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20170630	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20180419	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20201209	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vhsSource	VHSv20231101	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	videoSource	VIDEODR2	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	videoSource	VIDEODR3	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	videoSource	VIDEODR4	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	videoSource	VIDEODR5	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	videoSource	VIDEOv20111208	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingSource	VIKINGDR2	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingSource	VIKINGDR3	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingSource	VIKINGDR4	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingSource	VIKINGv20110714	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingSource	VIKINGv20111019	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingSource	VIKINGv20130417	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingSource	VIKINGv20140402	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingSource	VIKINGv20150421	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingSource	VIKINGv20151230	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingSource	VIKINGv20160406	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingSource	VIKINGv20161202	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingSource	VIKINGv20170715	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCDR1	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCDR2	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCDR3	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCDR4	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCDR5	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20110816	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCv20110909	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCv20120126	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCv20121128	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCv20130304	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr4	vmcSource	VMCv20130805	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20140428	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20140903	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20150309	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20151218	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20160311	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20160822	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20170109	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20170411	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20171101	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20180702	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20181120	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20191212	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20210708	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20230816	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20240226	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr4	vmcSource	VMCv20250130	Extended source Y aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	ultravistaSource	ULTRAVISTADR4	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vhsSource	VHSDR1	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vhsSource	VHSDR2	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vhsSource	VHSDR3	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSDR4	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSDR5	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSDR6	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSDR7	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20120926	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vhsSource	VHSv20130417	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vhsSource	VHSv20140409	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20150108	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20160114	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20160507	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20170630	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20180419	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20201209	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vhsSource	VHSv20231101	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	videoSource	VIDEODR2	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	videoSource	VIDEODR3	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	videoSource	VIDEODR4	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	videoSource	VIDEODR5	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	videoSource	VIDEOv20111208	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingSource	VIKINGDR2	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingSource	VIKINGDR3	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingSource	VIKINGDR4	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingSource	VIKINGv20110714	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingSource	VIKINGv20111019	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingSource	VIKINGv20130417	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingSource	VIKINGv20140402	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingSource	VIKINGv20150421	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingSource	VIKINGv20151230	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingSource	VIKINGv20160406	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingSource	VIKINGv20161202	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingSource	VIKINGv20170715	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCDR1	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCDR2	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCDR3	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCDR4	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCDR5	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20110816	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCv20110909	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCv20120126	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCv20121128	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCv20130304	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
yAperMagNoAperCorr6	vmcSource	VMCv20130805	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20140428	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20140903	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20150309	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20151218	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20160311	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20160822	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20170109	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20170411	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20171101	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20180702	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20181120	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20191212	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20210708	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20230816	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20240226	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yAperMagNoAperCorr6	vmcSource	VMCv20250130	Extended source Y aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yApFillFac	StackObjectAttributes	PS1DR2	Aperture fill factor from y filter stack detection.	real	4		-999
yApFlux	StackObjectAttributes	PS1DR2	Aperture flux from y filter stack detection.	real	4	Janskys	-999
yApFluxErr	StackObjectAttributes	PS1DR2	Error in aperture flux from y filter stack detection.	real	4	Janskys	-999
yApMag	StackObjectThin	PS1DR2	Aperture magnitude from y filter stack detection.	real	4	AB magnitudes	-999
yApMagErr	StackObjectThin	PS1DR2	Error in aperture magnitude from y filter stack detection.	real	4	AB magnitudes	-999
yApRadius	StackObjectAttributes	PS1DR2	Aperture radius for y filter stack detection.	real	4	arcsec	-999
yaStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vmcVarFrameSetInfo	VMCv20250130	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
yaStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vmcVarFrameSetInfo	VMCv20250130	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yaStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yAverageConf	ultravistaSourceRemeasurement	ULTRAVISTADR4	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSDR1	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vhsSource	VHSDR2	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vhsSource	VHSDR3	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSDR4	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSDR5	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSDR6	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSDR7	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20120926	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20130417	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20140409	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20150108	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20160114	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20160507	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20170630	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20180419	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20201209	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vhsSource	VHSv20231101	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGDR2	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vikingSource	VIKINGDR3	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGDR4	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20110714	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vikingSource	VIKINGv20111019	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vikingSource	VIKINGv20130417	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20140402	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20150421	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20151230	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20160406	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20161202	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingSource	VIKINGv20170715	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCDR2	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCDR3	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCDR4	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCDR5	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20110816	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vmcSource	VMCv20110909	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vmcSource	VMCv20120126	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vmcSource	VMCv20121128	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20130304	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20130805	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20140428	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20140903	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20150309	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20151218	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20160311	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20160822	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20170109	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20170411	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20171101	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20180702	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20181120	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20191212	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20210708	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20230816	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20240226	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource	VMCv20250130	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vmcSource, vmcSynopticSource	VMCDR1	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	meta.code
yAverageConf	vvvSource	VVVDR2	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vvvSource	VVVDR4	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
yAverageConf	vvvSource, vvvSynopticSource	VVVDR1	average confidence in 2 arcsec diameter default aperture (aper3) Y	real	4		-99999999	stat.likelihood;em.IR.NIR
YB	eros2LMCSource, eros2SMCSource, erosLMCSource, erosSMCSource	EROS	Y pixel coordinate on blue reference images relative to rebined reference images in [klmn] frame	real	4
ybestAper	ultravistaMapLcVariability	ULTRAVISTADR4	Best aperture (1-3) for photometric statistics in the Y 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)
ybestAper	ultravistaVariability	ULTRAVISTADR4	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	videoVariability	VIDEODR2	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	videoVariability	VIDEODR3	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	videoVariability	VIDEODR4	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	videoVariability	VIDEODR5	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	videoVariability	VIDEOv20100513	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	videoVariability	VIDEOv20111208	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vikingVariability	VIKINGv20110714	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCDR1	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCDR2	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCDR3	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCDR4	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCDR5	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20110816	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20110909	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20120126	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20121128	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20130304	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20130805	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20140428	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20140903	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20150309	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20151218	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20160311	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20160822	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20170109	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20170411	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20171101	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20180702	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20181120	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20191212	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20210708	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20230816	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20240226	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vmcVariability	VMCv20250130	Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper	vvvVariability	VVVDR5	Best aperture (1-6) for photometric statistics in the Y 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)
ybStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vmcVarFrameSetInfo	VMCv20250130	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ybStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vmcVarFrameSetInfo	VMCv20250130	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ybStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ychiSqAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	videoVarFrameSetInfo	VIDEODR2	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	videoVarFrameSetInfo	VIDEODR3	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	videoVarFrameSetInfo	VIDEODR4	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	videoVarFrameSetInfo	VIDEODR5	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	videoVarFrameSetInfo	VIDEOv20100513	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	videoVarFrameSetInfo	VIDEOv20111208	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vikingVarFrameSetInfo	VIKINGv20110714	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCDR1	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCDR2	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCDR3	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCDR4	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCDR5	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20110816	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20110909	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20120126	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20121128	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20130304	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20130805	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20140428	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20140903	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20150309	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20151218	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20160311	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20160822	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20170109	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20170411	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20171101	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20180702	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20181120	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20191212	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20210708	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20230816	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20240226	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vmcVarFrameSetInfo	VMCv20250130	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst	vvvVarFrameSetInfo	VVVDR5	Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqpd	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.
ychiSqpd	ultravistaVariability	ULTRAVISTADR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	videoVariability	VIDEODR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	videoVariability	VIDEODR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	vmcVariability	VMCDR3	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCDR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCDR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	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.
ychiSqpd	vmcVariability	VMCv20140428	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20140903	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20150309	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20151218	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20160311	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20160822	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20170109	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20170411	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20171101	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20180702	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20181120	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20191212	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20210708	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20230816	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20240226	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vmcVariability	VMCv20250130	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqpd	vvvVariability	VVVDR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;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.
ychiSqPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	videoVarFrameSetInfo	VIDEODR2	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	videoVarFrameSetInfo	VIDEODR3	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	videoVarFrameSetInfo	VIDEODR4	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	videoVarFrameSetInfo	VIDEODR5	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	videoVarFrameSetInfo	VIDEOv20100513	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	videoVarFrameSetInfo	VIDEOv20111208	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vikingVarFrameSetInfo	VIKINGv20110714	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCDR1	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCDR2	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCDR3	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCDR4	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCDR5	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20110816	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20110909	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20120126	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20121128	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20130304	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20130805	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20140428	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20140903	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20150309	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20151218	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20160311	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20160822	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20170109	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20170411	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20171101	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20180702	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20181120	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20191212	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20210708	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20230816	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20240226	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vmcVarFrameSetInfo	VMCv20250130	Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht	vvvVarFrameSetInfo	VVVDR5	Goodness of fit of Strateva function to photometric data in Y 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.
Yclass	vvvParallaxCatalogue	VVVDR5	VVV DR4 Y 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: Yclass}	int	4		-99999999
Yclass	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR4	VVV DR4 Y 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: Yclass}	int	4		-99999999
yClass	ultravistaSource	ULTRAVISTADR4	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	ultravistaSourceRemeasurement	ULTRAVISTADR4	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vhsSource	VHSDR2	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vhsSource	VHSDR3	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSDR4	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSDR5	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSDR6	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSDR7	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20120926	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vhsSource	VHSv20130417	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vhsSource	VHSv20140409	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20150108	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20160114	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20160507	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20170630	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20180419	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20201209	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource	VHSv20231101	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vhsSource, vhsSourceRemeasurement	VHSDR1	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	videoSource	VIDEODR2	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	videoSource	VIDEODR3	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	videoSource	VIDEODR4	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	videoSource	VIDEODR5	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	videoSource	VIDEOv20111208	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	videoSource, videoSourceRemeasurement	VIDEOv20100513	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingSource	VIKINGDR2	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingSource	VIKINGDR3	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingSource	VIKINGDR4	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vikingSource	VIKINGv20111019	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingSource	VIKINGv20130417	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingSource	VIKINGv20140402	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingSource	VIKINGv20150421	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vikingSource	VIKINGv20151230	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vikingSource	VIKINGv20160406	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vikingSource	VIKINGv20161202	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vikingSource	VIKINGv20170715	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCDR2	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCDR3	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCDR4	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCDR5	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20110909	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCv20120126	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCv20121128	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCv20130304	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCv20130805	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource	VMCv20140428	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20140903	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20150309	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20151218	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20160311	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20160822	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20170109	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20170411	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20171101	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20180702	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20181120	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20191212	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20210708	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20230816	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20240226	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource	VMCv20250130	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vmcSource, vmcSourceRemeasurement	VMCv20110816	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vmcSource, vmcSynopticSource	VMCDR1	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vvvSource	VVVDR2	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vvvSource	VVVDR4	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vvvSource	VVVDR5	discrete image classification flag in Y	smallint	2		-9999	src.class;em.IR.NIR
yClass	vvvSource	VVVv20110718	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vvvSource, vvvSourceRemeasurement	VVVv20100531	discrete image classification flag in Y	smallint	2		-9999	src.class
yClass	vvvSource, vvvSynopticSource	VVVDR1	discrete image classification flag in Y	smallint	2		-9999	src.class
yClassStat	ultravistaSource	ULTRAVISTADR4	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	ultravistaSourceRemeasurement	ULTRAVISTADR4	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vhsSource	VHSDR2	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vhsSource	VHSDR3	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSDR4	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSDR5	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSDR6	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSDR7	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20120926	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vhsSource	VHSv20130417	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vhsSource	VHSv20140409	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20150108	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20160114	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20160507	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20170630	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20180419	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20201209	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource	VHSv20231101	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vhsSource, vhsSourceRemeasurement	VHSDR1	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	videoSource	VIDEODR2	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	videoSource	VIDEODR3	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	videoSource	VIDEODR4	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	videoSource	VIDEODR5	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	videoSource	VIDEOv20100513	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	videoSource	VIDEOv20111208	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	videoSourceRemeasurement	VIDEOv20100513	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingSource	VIKINGDR2	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingSource	VIKINGDR3	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingSource	VIKINGDR4	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vikingSource	VIKINGv20111019	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingSource	VIKINGv20130417	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingSource	VIKINGv20140402	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingSource	VIKINGv20150421	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vikingSource	VIKINGv20151230	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vikingSource	VIKINGv20160406	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vikingSource	VIKINGv20161202	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vikingSource	VIKINGv20170715	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCDR2	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCDR3	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCDR4	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCDR5	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20110909	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCv20120126	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCv20121128	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCv20130304	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCv20130805	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource	VMCv20140428	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20140903	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20150309	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20151218	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20160311	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20160822	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20170109	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20170411	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20171101	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20180702	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20181120	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20191212	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20210708	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20230816	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20240226	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource	VMCv20250130	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vmcSource, vmcSourceRemeasurement	VMCv20110816	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vmcSource, vmcSynopticSource	VMCDR1	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSource	VVVDR1	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSource	VVVDR2	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSource	VVVDR4	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
yClassStat	vvvSource	VVVv20100531	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSource	VVVv20110718	S-Extractor classification statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSourceRemeasurement	VVVv20100531	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSourceRemeasurement	VVVv20110718	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSynopticSource	VVVDR1	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSynopticSource	VVVDR2	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat
yClassStat	vvvSynopticSource	VVVDR4	N(0,1) stellarness-of-profile statistic in Y	real	4		-0.9999995e9	stat;em.IR.NIR
ycStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vmcVarFrameSetInfo	VMCv20250130	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band.	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.
ycStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vmcVarFrameSetInfo	VMCv20250130	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
ycStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band.	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.
yDeblend	vhsSourceRemeasurement	VHSDR1	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	videoSource, videoSourceRemeasurement	VIDEOv20100513	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	vikingSourceRemeasurement	VIKINGv20110714	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	vikingSourceRemeasurement	VIKINGv20111019	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	vmcSourceRemeasurement	VMCv20110816	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	vmcSourceRemeasurement	VMCv20110909	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	vvvSource	VVVv20110718	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
yDeblend	vvvSource, vvvSourceRemeasurement	VVVv20100531	placeholder flag indicating parent/child relation in Y	int	4		-99999999	meta.code
ydec	StackObjectThin	PS1DR2	Declination from y filter stack detection.	float	8	degrees	-999
ydecErr	StackObjectThin	PS1DR2	Declination error from y filter stack detection.	real	4	arcsec	-999
Yell	vvvParallaxCatalogue	VVVDR5	Ellipticity of the DR4 Y detection. {catalogue TType keyword: Yell}	real	4		-999999500.0
Yell	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR4	Ellipticity of the DR4 Y detection. {catalogue TType keyword: Yell}	real	4		-999999500.0
yEll	ultravistaSource	ULTRAVISTADR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	ultravistaSourceRemeasurement	ULTRAVISTADR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticty
yEll	vhsSource	VHSDR2	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vhsSource	VHSDR3	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSDR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSDR5	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSDR6	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSDR7	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20120926	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vhsSource	VHSv20130417	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vhsSource	VHSv20140409	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20150108	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20160114	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20160507	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20170630	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20180419	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20201209	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource	VHSv20231101	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vhsSource, vhsSourceRemeasurement	VHSDR1	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	videoSource	VIDEODR2	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	videoSource	VIDEODR3	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	videoSource	VIDEODR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	videoSource	VIDEODR5	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	videoSource	VIDEOv20111208	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	videoSource, videoSourceRemeasurement	VIDEOv20100513	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingSource	VIKINGDR2	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingSource	VIKINGDR3	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingSource	VIKINGDR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vikingSource	VIKINGv20111019	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingSource	VIKINGv20130417	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingSource	VIKINGv20140402	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingSource	VIKINGv20150421	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vikingSource	VIKINGv20151230	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vikingSource	VIKINGv20160406	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vikingSource	VIKINGv20161202	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vikingSource	VIKINGv20170715	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCDR2	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCDR3	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCDR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCDR5	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20110909	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCv20120126	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCv20121128	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCv20130304	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCv20130805	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource	VMCv20140428	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20140903	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20150309	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20151218	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20160311	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20160822	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20170109	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20170411	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20171101	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20180702	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20181120	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20191212	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20210708	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20230816	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20240226	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource	VMCv20250130	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vmcSource, vmcSourceRemeasurement	VMCv20110816	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vmcSource, vmcSynopticSource	VMCDR1	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vvvSource	VVVDR2	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vvvSource	VVVDR4	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity;em.IR.NIR
yEll	vvvSource	VVVv20110718	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vvvSource, vvvSourceRemeasurement	VVVv20100531	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yEll	vvvSource, vvvSynopticSource	VVVDR1	1-b/a, where a/b=semi-major/minor axes in Y	real	4		-0.9999995e9	src.ellipticity
yeNum	ultravistaMergeLog, ultravistaRemeasMergeLog	ULTRAVISTADR4	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSDR1	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vhsMergeLog	VHSDR2	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vhsMergeLog	VHSDR3	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSDR4	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSDR5	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSDR6	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSDR7	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vhsMergeLog	VHSv20120926	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vhsMergeLog	VHSv20130417	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vhsMergeLog	VHSv20140409	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSv20150108	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSv20160114	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSv20160507	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSv20170630	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSv20180419	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vhsMergeLog	VHSv20201209	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vhsMergeLog	VHSv20231101	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	videoMergeLog	VIDEODR2	the extension number of this Y frame	tinyint	1			meta.number
yeNum	videoMergeLog	VIDEODR3	the extension number of this Y frame	tinyint	1			meta.number
yeNum	videoMergeLog	VIDEODR4	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	videoMergeLog	VIDEODR5	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	videoMergeLog	VIDEOv20100513	the extension number of this Y frame	tinyint	1			meta.number
yeNum	videoMergeLog	VIDEOv20111208	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGDR2	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGDR3	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGDR4	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vikingMergeLog	VIKINGv20110714	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGv20111019	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGv20130417	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGv20140402	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingMergeLog	VIKINGv20150421	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vikingMergeLog	VIKINGv20151230	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vikingMergeLog	VIKINGv20160406	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vikingMergeLog	VIKINGv20161202	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vikingMergeLog	VIKINGv20170715	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20170124	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCDR2	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCDR3	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCDR4	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCDR5	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vmcMergeLog	VMCv20110816	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCv20110909	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCv20120126	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCv20121128	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCv20130304	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCv20130805	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vmcMergeLog	VMCv20140428	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20140903	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20150309	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20151218	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20160311	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20160822	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20170109	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20170411	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20171101	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20180702	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20181120	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vmcMergeLog	VMCv20191212	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vmcMergeLog	VMCv20210708	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vmcMergeLog	VMCv20230816	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vmcMergeLog	VMCv20240226	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vmcMergeLog	VMCv20250130	the extension number of this Y frame	tinyint	1			meta.id;em.IR.NIR
yeNum	vmcMergeLog, vmcSynopticMergeLog	VMCDR1	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vvvMergeLog	VVVDR2	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vvvMergeLog	VVVDR4	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vvvMergeLog	VVVDR5	the extension number of this Y frame	tinyint	1			meta.number;em.IR.NIR
yeNum	vvvMergeLog	VVVv20100531	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vvvMergeLog	VVVv20110718	the extension number of this Y frame	tinyint	1			meta.number
yeNum	vvvMergeLog, vvvSynopticMergeLog	VVVDR1	the extension number of this Y frame	tinyint	1			meta.number
yEpoch	StackObjectThin	PS1DR2	Modified Julian Date of the mean epoch of images contributing to the the y-band stack (equinox J2000).	float	8	days	-999
Yerr	decapsSource	DECAPS	Uncertainty in mean Y-band flux (statistical only) {catalogue TType keyword: err[5]}	real	4	3631Jy	-9.999995e8	stat.error;phot.flux;em.IR.J
yErr	sharksDetection	SHARKSv20210222	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	sharksDetection	SHARKSv20210421	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	ultravistaDetection, ultravistaMapRemeasurement	ULTRAVISTADR4	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSDR2	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSDR3	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSDR4	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSDR5	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSDR6	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSDR7	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20120926	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20130417	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20140409	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20150108	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20160114	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20160507	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20170630	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20180419	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20201209	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection	VHSv20231101	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vhsDetection, vhsListRemeasurement	VHSDR1	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoDetection	VIDEODR2	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoDetection	VIDEODR3	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoDetection	VIDEODR4	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoDetection	VIDEODR5	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoDetection	VIDEOv20100513	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoDetection	VIDEOv20111208	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	videoListRemeasurement	VIDEOv20100513	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGDR2	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGDR3	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGDR4	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20111019	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20130417	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20140402	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20150421	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20151230	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20160406	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20161202	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection	VIKINGv20170715	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingDetection, vikingListRemeasurement	VIKINGv20110714	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingMapRemeasurement	VIKINGZYSELJv20160909	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vikingMapRemeasurement	VIKINGZYSELJv20170124	Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCDR1	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCDR2	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCDR3	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCDR4	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCDR5	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20110909	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20120126	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20121128	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20130304	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20130805	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20140428	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20140903	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20150309	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20151218	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20160311	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20160822	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20170109	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20170411	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20171101	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20180702	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20181120	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20191212	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20210708	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20230816	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20240226	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection	VMCv20250130	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcDetection, vmcListRemeasurement	VMCv20110816	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcdeepDetection	VMCDEEPv20230713	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vmcdeepDetection	VMCDEEPv20240506	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vvvDetection	VVVDR1	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vvvDetection	VVVDR2	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vvvDetection	VVVDR4	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
yErr	vvvDetection, vvvListRemeasurement	VVVv20100531	Error in Y coordinate {catalogue TType keyword: Y_coordinate_err} Estimate of centroid error.	real	4	pixels		stat.error
Yerr_lbs	decapsSource	DECAPS	Uncertainty in mean local background subtracted Y-band flux (statistical only) {catalogue TType keyword: err_lbs[5]}	real	4	3631Jy	-9.999995e8	stat.error;phot.flux;em.IR.J
YERR_R	spectra	SIXDF	error on Y_R position	int	4
YERR_V	spectra	SIXDF	error on Y_V position	int	4
yErrBits	ultravistaSource	ULTRAVISTADR4	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			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
yErrBits	ultravistaSourceRemeasurement	ULTRAVISTADR4	processing warning/error bitwise flags in Y	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.
yErrBits	vhsSource	VHSDR1	processing warning/error bitwise flags in Y	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.
yErrBits	vhsSource	VHSDR2	processing warning/error bitwise flags in Y	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.
yErrBits	vhsSource	VHSDR3	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSDR4	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSDR5	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSDR6	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSDR7	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20120926	processing warning/error bitwise flags in Y	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.
yErrBits	vhsSource	VHSv20130417	processing warning/error bitwise flags in Y	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.
yErrBits	vhsSource	VHSv20140409	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20150108	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20160114	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20160507	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20170630	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20180419	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20201209	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSource	VHSv20231101	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vhsSourceRemeasurement	VHSDR1	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	videoSource	VIDEODR2	processing warning/error bitwise flags in Y	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
yErrBits	videoSource	VIDEODR3	processing warning/error bitwise flags in Y	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
yErrBits	videoSource	VIDEODR4	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			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
yErrBits	videoSource	VIDEODR5	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			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
yErrBits	videoSource	VIDEOv20100513	processing warning/error bitwise flags in Y	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
yErrBits	videoSource	VIDEOv20111208	processing warning/error bitwise flags in Y	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
yErrBits	videoSourceRemeasurement	VIDEOv20100513	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	vikingSource	VIKINGDR2	processing warning/error bitwise flags in Y	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.
yErrBits	vikingSource	VIKINGDR3	processing warning/error bitwise flags in Y	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.
yErrBits	vikingSource	VIKINGDR4	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vikingSource	VIKINGv20110714	processing warning/error bitwise flags in Y	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.
yErrBits	vikingSource	VIKINGv20111019	processing warning/error bitwise flags in Y	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.
yErrBits	vikingSource	VIKINGv20130417	processing warning/error bitwise flags in Y	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.
yErrBits	vikingSource	VIKINGv20140402	processing warning/error bitwise flags in Y	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.
yErrBits	vikingSource	VIKINGv20150421	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vikingSource	VIKINGv20151230	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vikingSource	VIKINGv20160406	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vikingSource	VIKINGv20161202	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vikingSource	VIKINGv20170715	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vikingSourceRemeasurement	VIKINGv20110714	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	vikingSourceRemeasurement	VIKINGv20111019	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	processing warning/error bitwise flags in Y	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.
yErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCDR2	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCDR3	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCDR4	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCDR5	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20110816	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCv20110909	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCv20120126	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCv20121128	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCv20130304	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCv20130805	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSource	VMCv20140428	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20140903	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20150309	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20151218	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20160311	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20160822	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20170109	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20170411	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20171101	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20180702	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20181120	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20191212	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20210708	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20230816	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20240226	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource	VMCv20250130	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vmcSource, vmcSynopticSource	VMCDR1	processing warning/error bitwise flags in Y	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.
yErrBits	vmcSourceRemeasurement	VMCv20110816	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	vmcSourceRemeasurement	VMCv20110909	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	vvvSource	VVVDR2	processing warning/error bitwise flags in Y	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.
yErrBits	vvvSource	VVVDR4	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code;em.IR.NIR
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits	vvvSource	VVVv20100531	processing warning/error bitwise flags in Y	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.
yErrBits	vvvSource	VVVv20110718	processing warning/error bitwise flags in Y	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.
yErrBits	vvvSource, vvvSynopticSource	VVVDR1	processing warning/error bitwise flags in Y	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.
yErrBits	vvvSourceRemeasurement	VVVv20100531	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yErrBits	vvvSourceRemeasurement	VVVv20110718	processing warning/error bitwise flags in Y	int	4		-99999999	meta.code
yEta	ultravistaSource	ULTRAVISTADR4	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSDR1	Offset of Y 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.
yEta	vhsSource	VHSDR2	Offset of Y 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.
yEta	vhsSource	VHSDR3	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSDR4	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSDR5	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSDR6	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSDR7	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20120926	Offset of Y 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.
yEta	vhsSource	VHSv20130417	Offset of Y 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.
yEta	vhsSource	VHSv20140409	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20150108	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20160114	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20160507	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20170630	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20180419	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20201209	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vhsSource	VHSv20231101	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	videoSource	VIDEODR2	Offset of Y 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.
yEta	videoSource	VIDEODR3	Offset of Y 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.
yEta	videoSource	VIDEODR4	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	videoSource	VIDEODR5	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	videoSource	VIDEOv20100513	Offset of Y 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.
yEta	videoSource	VIDEOv20111208	Offset of Y 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.
yEta	vikingSource	VIKINGDR2	Offset of Y 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.
yEta	vikingSource	VIKINGDR3	Offset of Y 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.
yEta	vikingSource	VIKINGDR4	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vikingSource	VIKINGv20110714	Offset of Y 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.
yEta	vikingSource	VIKINGv20111019	Offset of Y 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.
yEta	vikingSource	VIKINGv20130417	Offset of Y 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.
yEta	vikingSource	VIKINGv20140402	Offset of Y 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.
yEta	vikingSource	VIKINGv20150421	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vikingSource	VIKINGv20151230	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vikingSource	VIKINGv20160406	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vikingSource	VIKINGv20161202	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vikingSource	VIKINGv20170715	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCDR2	Offset of Y 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.
yEta	vmcSource	VMCDR3	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCDR4	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCDR5	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20110816	Offset of Y 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.
yEta	vmcSource	VMCv20110909	Offset of Y 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.
yEta	vmcSource	VMCv20120126	Offset of Y 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.
yEta	vmcSource	VMCv20121128	Offset of Y 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.
yEta	vmcSource	VMCv20130304	Offset of Y 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.
yEta	vmcSource	VMCv20130805	Offset of Y 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.
yEta	vmcSource	VMCv20140428	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20140903	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20150309	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20151218	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20160311	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20160822	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20170109	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20170411	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20171101	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20180702	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20181120	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20191212	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20210708	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20230816	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20240226	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource	VMCv20250130	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vmcSource, vmcSynopticSource	VMCDR1	Offset of Y 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.
yEta	vvvSource	VVVDR2	Offset of Y 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.
yEta	vvvSource	VVVDR4	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vvvSource	VVVDR5	Offset of Y detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.NIR
			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.
yEta	vvvSource	VVVv20100531	Offset of Y 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.
yEta	vvvSource	VVVv20110718	Offset of Y 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.
yEta	vvvSource, vvvSynopticSource	VVVDR1	Offset of Y 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.
yexpML	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	videoVarFrameSetInfo	VIDEODR2	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	videoVarFrameSetInfo	VIDEODR3	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9	phot.mag;stat.max;em.IR.NIR
yexpML	videoVarFrameSetInfo	VIDEODR4	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	videoVarFrameSetInfo	VIDEODR5	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	videoVarFrameSetInfo	VIDEOv20100513	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	videoVarFrameSetInfo	VIDEOv20111208	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	vikingVarFrameSetInfo	VIKINGv20110714	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	vmcVarFrameSetInfo	VMCDR1	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	vmcVarFrameSetInfo	VMCDR2	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max;em.IR.NIR
yexpML	vmcVarFrameSetInfo	VMCDR3	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCDR4	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCDR5	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20110816	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	vmcVarFrameSetInfo	VMCv20110909	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	vmcVarFrameSetInfo	VMCv20120126	Expected magnitude limit of frameSet in this in Y band.	real	4		-0.9999995e9
yexpML	vmcVarFrameSetInfo	VMCv20121128	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
yexpML	vmcVarFrameSetInfo	VMCv20130304	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
yexpML	vmcVarFrameSetInfo	VMCv20130805	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max;em.IR.NIR
yexpML	vmcVarFrameSetInfo	VMCv20140428	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20140903	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20150309	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20151218	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20160311	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20160822	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20170109	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20170411	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20171101	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20180702	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20181120	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20191212	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20210708	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20230816	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20240226	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vmcVarFrameSetInfo	VMCv20250130	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yexpML	vvvVarFrameSetInfo	VVVDR5	Expected magnitude limit of frameSet in this in Y band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;stat.max
yExpRms	ultravistaMapLcVariability	ULTRAVISTADR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	ultravistaVariability	ULTRAVISTADR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	videoVariability	VIDEODR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	videoVariability	VIDEODR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	videoVariability	VIDEODR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	videoVariability	VIDEODR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	videoVariability	VIDEOv20100513	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	videoVariability	VIDEOv20111208	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vikingVariability	VIKINGv20110714	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCDR1	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCDR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCDR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCDR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCDR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20110816	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20110909	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20120126	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20121128	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20130304	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20130805	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20140428	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20140903	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20150309	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20151218	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20160311	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20160822	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20170109	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20170411	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20171101	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20180702	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20181120	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20191212	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20210708	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20230816	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20240226	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vmcVariability	VMCv20250130	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms	vvvVariability	VVVDR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yexpTime	StackObjectAttributes	PS1DR2	Exposure time of the y filter stack. Necessary for converting listed fluxes and magnitudes back to measured ADU counts.	real	4	seconds	-999
yExtent	RequiredMosaicTopLevel	SHARKSv20210222	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	SHARKSv20210421	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	ULTRAVISTADR4	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VHSDR7	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VHSv20201209	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VHSv20231101	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCDEEPv20230713	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCDEEPv20240506	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCDR5	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCv20191212	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCv20210708	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCv20230816	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCv20240226	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VMCv20250130	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VVVDR5	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtent	RequiredMosaicTopLevel	VVVXDR1	The angular extent of the mosaic image in the y-direction	real	4	degrees		??
yExtNSigma	StackObjectAttributes	PS1DR2	An extendedness measure for the y filter stack detection based on the deviation between PSF and Kron (1980) magnitudes, normalized by the PSF magnitude uncertainty.	real	4		-999
yFlags	MeanObject	PS1DR2	Information flag bitmask for mean object from y filter detections. Values listed in ObjectFilterFlags.	int	4		0
Yfracflux	decapsSource	DECAPS	PSF-weighted fraction of Y-band flux coming from this object (i.e., one minus the the fraction of flux in this object's PSF that comes from neighbouring objects?) {catalogue TType keyword: fracflux[5]}	real	4		-9.999995e8	stat.value;em.IR.J
yGausig	ultravistaSource	ULTRAVISTADR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	ultravistaSourceRemeasurement	ULTRAVISTADR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vhsSource	VHSDR2	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vhsSource	VHSDR3	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSDR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSDR5	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSDR6	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSDR7	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20120926	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vhsSource	VHSv20130417	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vhsSource	VHSv20140409	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20150108	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20160114	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20160507	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20170630	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20180419	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20201209	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource	VHSv20231101	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vhsSource, vhsSourceRemeasurement	VHSDR1	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	videoSource	VIDEODR2	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	videoSource	VIDEODR3	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	videoSource	VIDEODR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	videoSource	VIDEODR5	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	videoSource	VIDEOv20111208	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	videoSource, videoSourceRemeasurement	VIDEOv20100513	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingSource	VIKINGDR2	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingSource	VIKINGDR3	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingSource	VIKINGDR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vikingSource	VIKINGv20111019	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingSource	VIKINGv20130417	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingSource	VIKINGv20140402	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingSource	VIKINGv20150421	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vikingSource	VIKINGv20151230	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vikingSource	VIKINGv20160406	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vikingSource	VIKINGv20161202	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vikingSource	VIKINGv20170715	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCDR2	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCDR3	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCDR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCDR5	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20110909	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCv20120126	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCv20121128	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCv20130304	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCv20130805	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource	VMCv20140428	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20140903	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20150309	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20151218	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20160311	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20160822	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20170109	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20170411	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20171101	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20180702	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20181120	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20191212	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20210708	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20230816	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20240226	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource	VMCv20250130	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vmcSource, vmcSourceRemeasurement	VMCv20110816	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vmcSource, vmcSynopticSource	VMCDR1	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vvvSource	VVVDR2	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vvvSource	VVVDR4	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.NIR
yGausig	vvvSource	VVVv20110718	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vvvSource, vvvSourceRemeasurement	VVVv20100531	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yGausig	vvvSource, vvvSynopticSource	VVVDR1	RMS of axes of ellipse fit in Y	real	4	pixels	-0.9999995e9	src.morph.param
yHalfRad	ultravistaSource	ULTRAVISTADR4	SExtractor half-light radius in Y band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.NIR
yHalfRad	videoSource	VIDEODR4	SExtractor half-light radius in Y band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.NIR
yHalfRad	videoSource	VIDEODR5	SExtractor half-light radius in Y band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	ultravistaSource	ULTRAVISTADR4	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	ultravistaSourceRemeasurement	ULTRAVISTADR4	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	vhsSource	VHSDR1	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	vhsSource	VHSDR2	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	vhsSource	VHSDR3	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSDR4	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSDR5	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSDR6	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSDR7	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20120926	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	vhsSource	VHSv20130417	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	vhsSource	VHSv20140409	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20150108	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20160114	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20160507	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20170630	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20180419	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20201209	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vhsSource	VHSv20231101	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	videoSource	VIDEODR2	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	videoSource	VIDEODR3	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	videoSource	VIDEODR4	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	videoSource	VIDEODR5	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	videoSource	VIDEOv20100513	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	videoSource	VIDEOv20111208	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	vikingSource	VIKINGDR2	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	vikingSource	VIKINGDR3	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	vikingSource	VIKINGDR4	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vikingSource	VIKINGv20110714	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	vikingSource	VIKINGv20111019	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;src
yHlCorSMjRadAs	vikingSource	VIKINGv20130417	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	vikingSource	VIKINGv20140402	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize
yHlCorSMjRadAs	vikingSource	VIKINGv20150421	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vikingSource	VIKINGv20151230	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vikingSource	VIKINGv20160406	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vikingSource	VIKINGv20161202	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yHlCorSMjRadAs	vikingSource	VIKINGv20170715	Seeing corrected half-light, semi-major axis in Y band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.NIR
yinfoFlag	StackObjectThin	PS1DR2	Information flag bitmask indicating details of the y filter stack photometry. Values listed in DetectionFlags.	bigint	8		0
yinfoFlag2	StackObjectThin	PS1DR2	Information flag bitmask indicating details of the y filter stack photometry. Values listed in DetectionFlags2.	int	4		0
yinfoFlag3	StackObjectThin	PS1DR2	Information flag bitmask indicating details of the y filter stack photometry. Values listed in DetectionFlags3.	int	4		0
yIntRms	ultravistaMapLcVariability	ULTRAVISTADR4	Intrinsic rms in Y-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.
yIntRms	ultravistaVariability	ULTRAVISTADR4	Intrinsic rms in Y-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.
yIntRms	videoVariability	VIDEODR2	Intrinsic rms in Y-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.
yIntRms	videoVariability	VIDEODR3	Intrinsic rms in Y-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.
yIntRms	videoVariability	VIDEODR4	Intrinsic rms in Y-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.
yIntRms	videoVariability	VIDEODR5	Intrinsic rms in Y-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.
yIntRms	videoVariability	VIDEOv20100513	Intrinsic rms in Y-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.
yIntRms	videoVariability	VIDEOv20111208	Intrinsic rms in Y-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.
yIntRms	vikingVariability	VIKINGv20110714	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCDR1	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCDR2	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCDR3	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCDR4	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCDR5	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20110816	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20110909	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20120126	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20121128	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20130304	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20130805	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20140428	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20140903	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20150309	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20151218	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20160311	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20160822	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20170109	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20170411	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20171101	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20180702	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20181120	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20191212	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20210708	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20230816	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20240226	Intrinsic rms in Y-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.
yIntRms	vmcVariability	VMCv20250130	Intrinsic rms in Y-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.
yIntRms	vvvVariability	VVVDR5	Intrinsic rms in Y-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.
yippDetectID	StackObjectAttributes, StackObjectThin	PS1DR2	IPP internal detection identifier.	bigint	8
yisDefAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	videoVarFrameSetInfo	VIDEODR2	Use a default model for the astrometric noise in Y band.	tinyint	1		0
yisDefAst	videoVarFrameSetInfo	VIDEODR3	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	videoVarFrameSetInfo	VIDEODR4	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	videoVarFrameSetInfo	VIDEODR5	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	videoVarFrameSetInfo	VIDEOv20111208	Use a default model for the astrometric noise in Y band.	tinyint	1		0
yisDefAst	vmcVarFrameSetInfo	VMCDR1	Use a default model for the astrometric noise in Y band.	tinyint	1		0
yisDefAst	vmcVarFrameSetInfo	VMCDR2	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCDR3	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCDR4	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCDR5	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20110816	Use a default model for the astrometric noise in Y band.	tinyint	1		0
yisDefAst	vmcVarFrameSetInfo	VMCv20110909	Use a default model for the astrometric noise in Y band.	tinyint	1		0
yisDefAst	vmcVarFrameSetInfo	VMCv20120126	Use a default model for the astrometric noise in Y band.	tinyint	1		0
yisDefAst	vmcVarFrameSetInfo	VMCv20121128	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20130304	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20130805	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20140428	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20140903	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20150309	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20151218	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20160311	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20160822	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20170109	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20170411	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20171101	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20180702	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20181120	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20191212	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20210708	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20230816	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20240226	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vmcVarFrameSetInfo	VMCv20250130	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefAst	vvvVarFrameSetInfo	VVVDR5	Use a default model for the astrometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	videoVarFrameSetInfo	VIDEODR2	Use a default model for the photometric noise in Y band.	tinyint	1		0
yisDefPht	videoVarFrameSetInfo	VIDEODR3	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	videoVarFrameSetInfo	VIDEODR4	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	videoVarFrameSetInfo	VIDEODR5	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	videoVarFrameSetInfo	VIDEOv20111208	Use a default model for the photometric noise in Y band.	tinyint	1		0
yisDefPht	vmcVarFrameSetInfo	VMCDR1	Use a default model for the photometric noise in Y band.	tinyint	1		0
yisDefPht	vmcVarFrameSetInfo	VMCDR2	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCDR3	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCDR4	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCDR5	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20110816	Use a default model for the photometric noise in Y band.	tinyint	1		0
yisDefPht	vmcVarFrameSetInfo	VMCv20110909	Use a default model for the photometric noise in Y band.	tinyint	1		0
yisDefPht	vmcVarFrameSetInfo	VMCv20120126	Use a default model for the photometric noise in Y band.	tinyint	1		0
yisDefPht	vmcVarFrameSetInfo	VMCv20121128	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20130304	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20130805	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20140428	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20140903	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20150309	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20151218	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20160311	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20160822	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20170109	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20170411	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20171101	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20180702	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20181120	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20191212	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20210708	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20230816	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20240226	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vmcVarFrameSetInfo	VMCv20250130	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yisDefPht	vvvVarFrameSetInfo	VVVDR5	Use a default model for the photometric noise in Y band.	tinyint	1		0	meta.code;em.IR.NIR
yIsMeas	ultravistaSourceRemeasurement	ULTRAVISTADR4	Is pass band Y measured? 0 no, 1 yes	tinyint	1		0	meta.code
yIsMeas	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Is pass band Y measured? 0 no, 1 yes	tinyint	1		0	meta.code
yIsMeas	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Is pass band Y measured? 0 no, 1 yes	tinyint	1		0	meta.code
yjiWS	vmcVariability	VMCDR1	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCDR2	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCDR3	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCDR4	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCDR5	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20110816	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20110909	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20120126	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20121128	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20130304	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20130805	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20140428	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20140903	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20150309	Welch-Stetson statistic between Y and J. 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.
yjiWS	vmcVariability	VMCv20151218	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20160311	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20160822	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20170109	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20170411	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20171101	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20180702	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20181120	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20191212	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20210708	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20230816	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20240226	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yjiWS	vmcVariability	VMCv20250130	Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
			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.
yKronFlux	StackObjectAttributes	PS1DR2	Kron (1980) flux from y filter stack detection.	real	4	Janskys	-999
yKronFluxErr	StackObjectAttributes	PS1DR2	Error in Kron (1980) flux from y filter stack detection.	real	4	Janskys	-999
yKronJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y calibrated flux (Kron)	real	4	jansky	-0.9999995e9	phot.flux
yKronJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Y calibrated flux (Kron)	real	4	janksy	-0.9999995e9	stat.error
yKronLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y luptitude (Kron)	real	4	lup	-0.9999995e9	phot.lup
yKronLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Y luptitude (Kron)	real	4	lup	-0.9999995e9	stat.error
yKronMag	StackObjectThin	PS1DR2	Kron (1980) magnitude from y filter stack detection.	real	4	AB magnitudes	-999
yKronMag	ultravistaSource	ULTRAVISTADR4	Extended source Y mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yKronMag	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y magnitude (Kron)	real	4	mag	-0.9999995e9	phot.mag
yKronMag	videoSource	VIDEODR4	Extended source Y mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yKronMag	videoSource	VIDEODR5	Extended source Y mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yKronMagErr	StackObjectThin	PS1DR2	Error in Kron (1980) magnitude from y filter stack detection.	real	4	AB magnitudes	-999
yKronMagErr	ultravistaSource	ULTRAVISTADR4	Extended source Y mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yKronMagErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Y magnitude (Kron)	real	4	mag	-0.9999995e9	stat.error
yKronMagErr	videoSource	VIDEODR4	Extended source Y mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yKronMagErr	videoSource	VIDEODR5	Extended source Y mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yKronRad	StackObjectAttributes	PS1DR2	Kron (1980) radius from y filter stack detection.	real	4	arcsec	-999
Ymag	vvvParallaxCatalogue	VVVDR5	VVV DR4 Y photometry {catalogue TType keyword: Ymag}	real	4	mag	-999999500.0
Ymag	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR4	VVV DR4 Y photometry {catalogue TType keyword: Ymag}	real	4	mag	-999999500.0
yMag	vhsSourceRemeasurement	VHSDR1	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	videoSourceRemeasurement	VIDEOv20100513	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	vikingSourceRemeasurement	VIKINGv20110714	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	vikingSourceRemeasurement	VIKINGv20111019	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	vmcLmc75Variables	VMCv20250130	Apparent Y band magnitude from VMC deep stack {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yMag	vmcQsos	VMCv20250130	Y band magnitude from VMC {catalogue TType keyword: y}	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;meta.main
yMag	vmcSourceRemeasurement	VMCv20110816	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	vmcSourceRemeasurement	VMCv20110909	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	vvvSourceRemeasurement	VVVv20100531	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMag	vvvSourceRemeasurement	VVVv20110718	Y mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
yMagErr	vhsSourceRemeasurement	VHSDR1	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	videoSourceRemeasurement	VIDEOv20100513	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	vikingSourceRemeasurement	VIKINGv20110714	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	vikingSourceRemeasurement	VIKINGv20111019	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	vmcCepheidVariables	VMCDR4	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20160311	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20160822	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20170109	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20170411	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20171101	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20180702	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20181120	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20191212	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20210708	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20230816	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20240226	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcCepheidVariables	VMCv20250130	Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcQsos	VMCv20250130	Error on Y band magnitude {catalogue TType keyword: yerr}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yMagErr	vmcSourceRemeasurement	VMCv20110816	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	vmcSourceRemeasurement	VMCv20110909	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	vvvSourceRemeasurement	VVVv20100531	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
yMagErr	vvvSourceRemeasurement	VVVv20110718	Error in Y mag	real	4	mag	-0.9999995e9	stat.error
Ymaglimit	decapsSource	DECAPS	5 sigma Y-band magnitude limit for deepest detection of this object (AB mag) {catalogue TType keyword: maglimit[5]}	real	4	mag	-9.999995e8	phot.mag;em.IR.J
yMagMAD	ultravistaMapLcVariability	ULTRAVISTADR4	Median Absolute Deviation of Y 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.
yMagMAD	ultravistaVariability	ULTRAVISTADR4	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	videoVariability	VIDEODR2	Median Absolute Deviation of Y 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.
yMagMAD	videoVariability	VIDEODR3	Median Absolute Deviation of Y 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.
yMagMAD	videoVariability	VIDEODR4	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	videoVariability	VIDEODR5	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	videoVariability	VIDEOv20100513	Median Absolute Deviation of Y 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.
yMagMAD	videoVariability	VIDEOv20111208	Median Absolute Deviation of Y 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.
yMagMAD	vikingVariability	VIKINGv20110714	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCDR1	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCDR2	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCDR3	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCDR4	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCDR5	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20110816	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCv20110909	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCv20120126	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCv20121128	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCv20130304	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCv20130805	Median Absolute Deviation of Y 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.
yMagMAD	vmcVariability	VMCv20140428	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;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.
yMagMAD	vmcVariability	VMCv20140903	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20150309	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20151218	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20160311	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20160822	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20170109	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20170411	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20171101	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20180702	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20181120	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20191212	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20210708	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20230816	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20240226	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vmcVariability	VMCv20250130	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagMAD	vvvVariability	VVVDR5	Median Absolute Deviation of Y magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.NIR;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.
yMagRms	ultravistaMapLcVariability	ULTRAVISTADR4	rms of Y 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.
yMagRms	ultravistaVariability	ULTRAVISTADR4	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	videoVariability	VIDEODR2	rms of Y 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.
yMagRms	videoVariability	VIDEODR3	rms of Y 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.
yMagRms	videoVariability	VIDEODR4	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;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.
yMagRms	videoVariability	VIDEODR5	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;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.
yMagRms	videoVariability	VIDEOv20100513	rms of Y 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.
yMagRms	videoVariability	VIDEOv20111208	rms of Y 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.
yMagRms	vikingVariability	VIKINGv20110714	rms of Y 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.
yMagRms	vmcVariability	VMCDR1	rms of Y 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.
yMagRms	vmcVariability	VMCDR2	rms of Y 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.
yMagRms	vmcVariability	VMCDR3	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;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.
yMagRms	vmcVariability	VMCDR4	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCDR5	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20110816	rms of Y 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.
yMagRms	vmcVariability	VMCv20110909	rms of Y 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.
yMagRms	vmcVariability	VMCv20120126	rms of Y 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.
yMagRms	vmcVariability	VMCv20121128	rms of Y 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.
yMagRms	vmcVariability	VMCv20130304	rms of Y 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.
yMagRms	vmcVariability	VMCv20130805	rms of Y 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.
yMagRms	vmcVariability	VMCv20140428	rms of Y 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.
yMagRms	vmcVariability	VMCv20140903	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;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.
yMagRms	vmcVariability	VMCv20150309	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;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.
yMagRms	vmcVariability	VMCv20151218	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20160311	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20160822	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20170109	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20170411	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20171101	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20180702	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20181120	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20191212	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20210708	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20230816	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20240226	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vmcVariability	VMCv20250130	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
yMagRms	vvvVariability	VVVDR5	rms of Y magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	vmcVariability	VMCv20140428	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max;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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	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.
ymaxCadence	vmcVariability	VMCv20250130	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.
ymaxCadence	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.
yMaxMag	ultravistaMapLcVariability	ULTRAVISTADR4	Maximum magnitude in Y 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.
yMaxMag	ultravistaVariability	ULTRAVISTADR4	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	videoVariability	VIDEODR2	Maximum magnitude in Y 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.
yMaxMag	videoVariability	VIDEODR3	Maximum magnitude in Y 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.
yMaxMag	videoVariability	VIDEODR4	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	videoVariability	VIDEODR5	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	videoVariability	VIDEOv20100513	Maximum magnitude in Y 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.
yMaxMag	videoVariability	VIDEOv20111208	Maximum magnitude in Y 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.
yMaxMag	vikingVariability	VIKINGv20110714	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCDR1	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCDR2	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCDR3	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCDR4	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCDR5	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20110816	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCv20110909	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCv20120126	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCv20121128	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCv20130304	Maximum magnitude in Y 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.
yMaxMag	vmcVariability	VMCv20130805	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20140428	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20140903	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20150309	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20151218	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20160311	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20160822	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20170109	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20170411	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20171101	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20180702	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20181120	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20191212	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20210708	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20230816	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20240226	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vmcVariability	VMCv20250130	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMaxMag	vvvVariability	VVVDR5	Maximum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
Ymean	decapsSource	DECAPS	Y-band mean flux, good detections only (3631 Jy) {catalogue TType keyword: mean[5]}	real	4	3631Jy		phot.flux;em.IR.J
yMean	vmcRRLyraeVariables	VMCv20240226	Mean Y band magnitude derived with GRATIS {catalogue TType keyword: Y_MEAN}	real	4	mag		phot.mag;stat.mean;em.IR.NIR
Ymean_lbs	decapsSource	DECAPS	Mean Y-band flux, using a local background subtraction (3631 Jy) {catalogue TType keyword: mean_lbs[5]}	real	4	3631Jy	-9.999995e8	stat.mean;phot.flux;em.IR.J
yMeanApMag	MeanObject	PS1DR2	Mean aperture magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanApMagErr	MeanObject	PS1DR2	Error in mean aperture magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanApMagNpt	MeanObject	PS1DR2	Number of measurements included in mean aperture magnitude from y filter detections.	smallint	2		-999
yMeanApMagStd	MeanObject	PS1DR2	Standard deviation of aperture magnitudes from y filter detections.	real	4	AB magnitudes	-999
yMeanKronMag	MeanObject	PS1DR2	Mean Kron (1980) magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanKronMagErr	MeanObject	PS1DR2	Error in mean Kron (1980) magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanKronMagNpt	MeanObject	PS1DR2	Number of measurements included in mean Kron (1980) magnitude from y filter detections.	smallint	2		-999
yMeanKronMagStd	MeanObject	PS1DR2	Standard deviation of Kron (1980) magnitudes from y filter detections.	real	4	AB magnitudes	-999
yMeanMag	vmcCepheidVariables	VMCDR4	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20160311	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20160822	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20170109	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20170411	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20171101	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20180702	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20181120	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20191212	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20210708	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20230816	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20240226	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
yMeanMag	vmcCepheidVariables	VMCv20250130	Intensity-averaged Y band magnitude {catalogue TType keyword: Ymag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
ymeanMag	ultravistaMapLcVariability	ULTRAVISTADR4	Mean Y 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.
ymeanMag	ultravistaVariability	ULTRAVISTADR4	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	videoVariability	VIDEODR2	Mean Y 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.
ymeanMag	videoVariability	VIDEODR3	Mean Y 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.
ymeanMag	videoVariability	VIDEODR4	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	videoVariability	VIDEODR5	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	videoVariability	VIDEOv20100513	Mean Y 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.
ymeanMag	videoVariability	VIDEOv20111208	Mean Y 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.
ymeanMag	vikingVariability	VIKINGv20110714	Mean Y 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.
ymeanMag	vmcVariability	VMCDR1	Mean Y 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.
ymeanMag	vmcVariability	VMCDR2	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCDR3	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCDR4	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCDR5	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20110816	Mean Y 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.
ymeanMag	vmcVariability	VMCv20110909	Mean Y 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.
ymeanMag	vmcVariability	VMCv20120126	Mean Y 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.
ymeanMag	vmcVariability	VMCv20121128	Mean Y 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.
ymeanMag	vmcVariability	VMCv20130304	Mean Y 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.
ymeanMag	vmcVariability	VMCv20130805	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20140428	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20140903	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20150309	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20151218	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20160311	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20160822	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20170109	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20170411	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20171101	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20180702	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20181120	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20191212	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20210708	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20230816	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20240226	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vmcVariability	VMCv20250130	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymeanMag	vvvVariability	VVVDR5	Mean Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMeanPSFMag	MeanObject	PS1DR2	Mean PSF magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanPSFMagErr	MeanObject	PS1DR2	Error in mean PSF magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanPSFMagMax	MeanObject	PS1DR2	Maximum PSF magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanPSFMagMin	MeanObject	PS1DR2	Minimum PSF magnitude from y filter detections.	real	4	AB magnitudes	-999
yMeanPSFMagNpt	MeanObject	PS1DR2	Number of measurements included in mean PSF magnitude from y filter detections.	smallint	2		-999
yMeanPSFMagStd	MeanObject	PS1DR2	Standard deviation of PSF magnitudes from y filter detections.	real	4	AB magnitudes	-999
Ymeas	vvvProperMotionCatalogue	VVVDR4	Is there a Y band measurment for this frame	tinyint	1
Ymeas	vvvProperMotionCatalogue	VVVDR5	Is there a Y band measurment for this frame	tinyint	1
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	vmcVariability	VMCv20140428	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median;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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	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.
ymedCadence	vmcVariability	VMCv20250130	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.
ymedCadence	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.
Ymedian	decapsSource	DECAPS	Median Y-band flux (3631 Jy) {catalogue TType keyword: median[5]}	real	4	3631Jy	-9.999995e8	stat.median;phot.flux;em.IR.J
Ymedian_lbs	decapsSource	DECAPS	Median local background subtracted Y-band flux (3631 Jy) {catalogue TType keyword: median_lbs[5]}	real	4	3631Jy	-9.999995e8	stat.median;phot.flux;em.IR.J
ymedianMag	ultravistaMapLcVariability	ULTRAVISTADR4	Median Y 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.
ymedianMag	ultravistaVariability	ULTRAVISTADR4	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	videoVariability	VIDEODR2	Median Y 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.
ymedianMag	videoVariability	VIDEODR3	Median Y 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.
ymedianMag	videoVariability	VIDEODR4	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	videoVariability	VIDEODR5	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	videoVariability	VIDEOv20100513	Median Y 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.
ymedianMag	videoVariability	VIDEOv20111208	Median Y 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.
ymedianMag	vikingVariability	VIKINGv20110714	Median Y 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.
ymedianMag	vmcVariability	VMCDR1	Median Y 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.
ymedianMag	vmcVariability	VMCDR2	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCDR3	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCDR4	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCDR5	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20110816	Median Y 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.
ymedianMag	vmcVariability	VMCv20110909	Median Y 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.
ymedianMag	vmcVariability	VMCv20120126	Median Y 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.
ymedianMag	vmcVariability	VMCv20121128	Median Y 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.
ymedianMag	vmcVariability	VMCv20130304	Median Y 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.
ymedianMag	vmcVariability	VMCv20130805	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20140428	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20140903	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20150309	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20151218	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20160311	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20160822	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20170109	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20170411	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20171101	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20180702	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20181120	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20191212	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20210708	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20230816	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20240226	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vmcVariability	VMCv20250130	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymedianMag	vvvVariability	VVVDR5	Median Y magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymfID	ultravistaMergeLog, ultravistaRemeasMergeLog	ULTRAVISTADR4	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSDR1	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vhsMergeLog	VHSDR2	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vhsMergeLog	VHSDR3	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSDR4	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSDR5	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSDR6	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSDR7	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20120926	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vhsMergeLog	VHSv20130417	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vhsMergeLog	VHSv20140409	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20150108	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20160114	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20160507	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20170630	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20180419	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20201209	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vhsMergeLog	VHSv20231101	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	videoMergeLog	VIDEODR2	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	videoMergeLog	VIDEODR3	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	videoMergeLog	VIDEODR4	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	videoMergeLog	VIDEODR5	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	videoMergeLog	VIDEOv20100513	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	videoMergeLog	VIDEOv20111208	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vikingMergeLog	VIKINGDR2	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vikingMergeLog	VIKINGDR3	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vikingMergeLog	VIKINGDR4	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vikingMergeLog	VIKINGv20110714	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vikingMergeLog	VIKINGv20111019	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vikingMergeLog	VIKINGv20130417	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vikingMergeLog	VIKINGv20140402	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vikingMergeLog	VIKINGv20150421	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vikingMergeLog	VIKINGv20151230	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vikingMergeLog	VIKINGv20160406	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vikingMergeLog	VIKINGv20161202	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vikingMergeLog	VIKINGv20170715	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20170124	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vmcMergeLog	VMCDR2	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vmcMergeLog	VMCDR3	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCDR4	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCDR5	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20110816	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vmcMergeLog	VMCv20110909	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vmcMergeLog	VMCv20120126	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vmcMergeLog	VMCv20121128	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vmcMergeLog	VMCv20130304	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vmcMergeLog	VMCv20130805	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vmcMergeLog	VMCv20140428	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20140903	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20150309	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20151218	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20160311	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20160822	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20170109	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20170411	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20171101	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20180702	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20181120	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20191212	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20210708	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20230816	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20240226	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog	VMCv20250130	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vmcMergeLog, vmcSynopticMergeLog	VMCDR1	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vvvMergeLog	VVVDR2	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
ymfID	vvvMergeLog	VVVDR4	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vvvMergeLog	VVVDR5	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field;em.IR.NIR
ymfID	vvvMergeLog	VVVv20100531	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vvvMergeLog	VVVv20110718	the UID of the relevant Y multiframe	bigint	8			obs.field
ymfID	vvvMergeLog, vvvSynopticMergeLog	VVVDR1	the UID of the relevant Y multiframe	bigint	8			meta.id;obs.field
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	vmcVariability	VMCv20140428	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min;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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	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.
yminCadence	vmcVariability	VMCv20250130	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.
yminCadence	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.
yMinMag	ultravistaMapLcVariability	ULTRAVISTADR4	Minimum magnitude in Y 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.
yMinMag	ultravistaVariability	ULTRAVISTADR4	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	videoVariability	VIDEODR2	Minimum magnitude in Y 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.
yMinMag	videoVariability	VIDEODR3	Minimum magnitude in Y 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.
yMinMag	videoVariability	VIDEODR4	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	videoVariability	VIDEODR5	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	videoVariability	VIDEOv20100513	Minimum magnitude in Y 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.
yMinMag	videoVariability	VIDEOv20111208	Minimum magnitude in Y 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.
yMinMag	vikingVariability	VIKINGv20110714	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCDR1	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCDR2	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCDR3	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCDR4	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCDR5	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20110816	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCv20110909	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCv20120126	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCv20121128	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCv20130304	Minimum magnitude in Y 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.
yMinMag	vmcVariability	VMCv20130805	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20140428	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20140903	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20150309	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20151218	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20160311	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20160822	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20170109	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20170411	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20171101	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20180702	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20181120	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20191212	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20210708	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20230816	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20240226	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vmcVariability	VMCv20250130	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
yMinMag	vvvVariability	VVVDR5	Minimum magnitude in Y band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR;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.
ymj	vhsSourceRemeasurement	VHSDR1	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	videoSourceRemeasurement	VIDEOv20100513	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	vikingSourceRemeasurement	VIKINGv20110714	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	vikingSourceRemeasurement	VIKINGv20111019	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	vmcSourceRemeasurement	VMCv20110816	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	vmcSourceRemeasurement	VMCv20110909	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	vvvSourceRemeasurement	VVVv20100531	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj	vvvSourceRemeasurement	VVVv20110718	Default colour Y-J (using appropriate mags)	real	4	mag		PHOT_COLOR
ymj_2Ext	vikingSource	VIKINGv20151230	Extended source colour Y-J_2 (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Ext	vikingSource	VIKINGv20160406	Extended source colour Y-J_2 (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Ext	vikingSource	VIKINGv20161202	Extended source colour Y-J_2 (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Ext	vikingSource	VIKINGv20170715	Extended source colour Y-J_2 (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2ExtErr	vikingSource	VIKINGv20151230	Error on extended source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2ExtErr	vikingSource	VIKINGv20160406	Error on extended source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2ExtErr	vikingSource	VIKINGv20161202	Error on extended source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2ExtErr	vikingSource	VIKINGv20170715	Error on extended source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Pnt	vikingSource	VIKINGv20151230	Point source colour Y-J_2 (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Pnt	vikingSource	VIKINGv20160406	Point source colour Y-J_2 (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Pnt	vikingSource	VIKINGv20161202	Point source colour Y-J_2 (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2Pnt	vikingSource	VIKINGv20170715	Point source colour Y-J_2 (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2PntErr	vikingSource	VIKINGv20151230	Error on point source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2PntErr	vikingSource	VIKINGv20160406	Error on point source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2PntErr	vikingSource	VIKINGv20161202	Error on point source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymj_2PntErr	vikingSource	VIKINGv20170715	Error on point source colour Y-J_2	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
yMjd	ultravistaSource	ULTRAVISTADR4	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	ultravistaSourceRemeasurement	ULTRAVISTADR4	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vhsSource	VHSDR5	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vhsSource	VHSDR6	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vhsSource	VHSDR7	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vhsSource	VHSv20160114	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vhsSource	VHSv20160507	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vhsSource	VHSv20170630	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vhsSource	VHSv20180419	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vhsSource	VHSv20201209	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vhsSource	VHSv20231101	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vikingSource	VIKINGv20151230	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vikingSource	VIKINGv20160406	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vikingSource	VIKINGv20161202	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vikingSource	VIKINGv20170715	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vmcSource	VMCDR5	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20151218	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vmcSource	VMCv20160311	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vmcSource	VMCv20160822	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vmcSource	VMCv20170109	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vmcSource	VMCv20170411	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vmcSource	VMCv20171101	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20180702	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20181120	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20191212	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20210708	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20230816	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20240226	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource	VMCv20250130	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vmcSource, vmcSynopticSource	VMCDR4	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vvvSource	VVVDR5	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch;em.IR.NIR
yMjd	vvvSynopticSource	VVVDR1	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vvvSynopticSource	VVVDR2	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
yMjd	vvvSynopticSource	VVVDR4	Modified Julian Day in Y band	float	8	days	-0.9999995e9	time.epoch
ymjErr	vhsSourceRemeasurement	VHSDR1	Error on colour Y-J	real	4	mag		stat.error
ymjErr	videoSourceRemeasurement	VIDEOv20100513	Error on colour Y-J	real	4	mag		stat.error
ymjErr	vikingSourceRemeasurement	VIKINGv20110714	Error on colour Y-J	real	4	mag		stat.error
ymjErr	vikingSourceRemeasurement	VIKINGv20111019	Error on colour Y-J	real	4	mag		stat.error
ymjErr	vmcSourceRemeasurement	VMCv20110816	Error on colour Y-J	real	4	mag		stat.error
ymjErr	vmcSourceRemeasurement	VMCv20110909	Error on colour Y-J	real	4	mag		stat.error
ymjErr	vvvSourceRemeasurement	VVVv20100531	Error on colour Y-J	real	4	mag		stat.error
ymjErr	vvvSourceRemeasurement	VVVv20110718	Error on colour Y-J	real	4	mag		stat.error
ymjExt	ultravistaSource	ULTRAVISTADR4	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source colour Y-J (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.
ymjExt	vhsSource	VHSDR1	Extended source colour Y-J (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.
ymjExt	vhsSource	VHSDR2	Extended source colour Y-J (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.
ymjExt	vhsSource	VHSDR3	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSDR4	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSDR5	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSDR6	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSDR7	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20120926	Extended source colour Y-J (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.
ymjExt	vhsSource	VHSv20130417	Extended source colour Y-J (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.
ymjExt	vhsSource	VHSv20140409	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20150108	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20160114	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20160507	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20170630	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20180419	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20201209	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vhsSource	VHSv20231101	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	videoSource	VIDEODR2	Extended source colour Y-J (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.
ymjExt	videoSource	VIDEODR3	Extended source colour Y-J (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.
ymjExt	videoSource	VIDEODR4	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	videoSource	VIDEODR5	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	videoSource	VIDEOv20100513	Extended source colour Y-J (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.
ymjExt	videoSource	VIDEOv20111208	Extended source colour Y-J (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.
ymjExt	vikingSource	VIKINGDR2	Extended source colour Y-J (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.
ymjExt	vikingSource	VIKINGDR3	Extended source colour Y-J (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.
ymjExt	vikingSource	VIKINGDR4	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingSource	VIKINGv20110714	Extended source colour Y-J (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.
ymjExt	vikingSource	VIKINGv20111019	Extended source colour Y-J (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.
ymjExt	vikingSource	VIKINGv20130417	Extended source colour Y-J (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.
ymjExt	vikingSource	VIKINGv20140402	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingSource	VIKINGv20150421	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingSource	VIKINGv20151230	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingSource	VIKINGv20160406	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingSource	VIKINGv20161202	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingSource	VIKINGv20170715	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source colour Y-J (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.
ymjExt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCDR1	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCDR2	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCDR3	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCDR4	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCDR5	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20110816	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCv20110909	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCv20120126	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCv20121128	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCv20130304	Extended source colour Y-J (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.
ymjExt	vmcSource	VMCv20130805	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20140428	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20140903	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20150309	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20151218	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20160311	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20160822	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20170109	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20170411	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20171101	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20180702	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20181120	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20191212	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20210708	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20230816	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20240226	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vmcSource	VMCv20250130	Extended source colour Y-J (using aperMagNoAperCorr3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExt	vvvSource	VVVv20100531	Extended source colour Y-J (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.
ymjExtErr	ultravistaSource	ULTRAVISTADR4	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on extended source colour Y-J	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.
ymjExtErr	vhsSource	VHSDR1	Error on extended source colour Y-J	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.
ymjExtErr	vhsSource	VHSDR2	Error on extended source colour Y-J	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.
ymjExtErr	vhsSource	VHSDR3	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSDR4	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSDR5	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSDR6	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSDR7	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20120926	Error on extended source colour Y-J	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.
ymjExtErr	vhsSource	VHSv20130417	Error on extended source colour Y-J	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.
ymjExtErr	vhsSource	VHSv20140409	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20150108	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20160114	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20160507	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20170630	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20180419	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20201209	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vhsSource	VHSv20231101	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	videoSource	VIDEODR2	Error on extended source colour Y-J	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.
ymjExtErr	videoSource	VIDEODR3	Error on extended source colour Y-J	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.
ymjExtErr	videoSource	VIDEODR4	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	videoSource	VIDEODR5	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	videoSource	VIDEOv20100513	Error on extended source colour Y-J	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.
ymjExtErr	videoSource	VIDEOv20111208	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGDR2	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGDR3	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGDR4	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vikingSource	VIKINGv20110714	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGv20111019	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGv20130417	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGv20140402	Error on extended source colour Y-J	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.
ymjExtErr	vikingSource	VIKINGv20150421	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vikingSource	VIKINGv20151230	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vikingSource	VIKINGv20160406	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vikingSource	VIKINGv20161202	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vikingSource	VIKINGv20170715	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on extended source colour Y-J	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.
ymjExtErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCDR1	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCDR2	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCDR3	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCDR4	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCDR5	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20110816	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCv20110909	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCv20120126	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCv20121128	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCv20130304	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCv20130805	Error on extended source colour Y-J	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.
ymjExtErr	vmcSource	VMCv20140428	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20140903	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20150309	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20151218	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20160311	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20160822	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20170109	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20170411	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20171101	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20180702	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20181120	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20191212	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20210708	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20230816	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20240226	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vmcSource	VMCv20250130	Error on extended source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjExtErr	vvvSource	VVVv20100531	Error on extended source colour Y-J	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.
ymjExtJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source colour calibrated flux J/Y (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.
ymjExtJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source colour calibrated flux J/Y (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.
ymjExtJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source colour calibrated flux J/Y (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.
ymjExtJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on extended source colour calibrated flux J/Y	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.
ymjExtJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on extended source colour calibrated flux J/Y	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.
ymjExtJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on extended source colour calibrated flux J/Y	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.
ymjExtLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source colour luptitudeY-J (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.
ymjExtLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source colour luptitudeY-J (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.
ymjExtLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source colour luptitudeY-J (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.
ymjExtLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on extended source colour luptitude Y-J	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.
ymjExtLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on extended source colour luptitude Y-J	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.
ymjExtLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on extended source colour luptitude Y-J	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.
ymjPnt	ultravistaSource	ULTRAVISTADR4	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source colour Y-J (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.
ymjPnt	vhsSource	VHSDR1	Point source colour Y-J (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.
ymjPnt	vhsSource	VHSDR2	Point source colour Y-J (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.
ymjPnt	vhsSource	VHSDR3	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSDR4	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSDR5	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSDR6	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSDR7	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20120926	Point source colour Y-J (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.
ymjPnt	vhsSource	VHSv20130417	Point source colour Y-J (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.
ymjPnt	vhsSource	VHSv20140409	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20150108	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20160114	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20160507	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20170630	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20180419	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20201209	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vhsSource	VHSv20231101	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	videoSource	VIDEODR2	Point source colour Y-J (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.
ymjPnt	videoSource	VIDEODR3	Point source colour Y-J (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.
ymjPnt	videoSource	VIDEODR4	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	videoSource	VIDEODR5	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	videoSource	VIDEOv20100513	Point source colour Y-J (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.
ymjPnt	videoSource	VIDEOv20111208	Point source colour Y-J (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.
ymjPnt	vikingSource	VIKINGDR2	Point source colour Y-J (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.
ymjPnt	vikingSource	VIKINGDR3	Point source colour Y-J (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.
ymjPnt	vikingSource	VIKINGDR4	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingSource	VIKINGv20110714	Point source colour Y-J (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.
ymjPnt	vikingSource	VIKINGv20111019	Point source colour Y-J (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.
ymjPnt	vikingSource	VIKINGv20130417	Point source colour Y-J (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.
ymjPnt	vikingSource	VIKINGv20140402	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingSource	VIKINGv20150421	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingSource	VIKINGv20151230	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingSource	VIKINGv20160406	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingSource	VIKINGv20161202	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingSource	VIKINGv20170715	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source colour Y-J (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.
ymjPnt	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source colour Y-J (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.
ymjPnt	vmcSource	VMCDR2	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCDR3	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCDR4	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCDR5	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20110816	Point source colour Y-J (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.
ymjPnt	vmcSource	VMCv20110909	Point source colour Y-J (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.
ymjPnt	vmcSource	VMCv20120126	Point source colour Y-J (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.
ymjPnt	vmcSource	VMCv20121128	Point source colour Y-J (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.
ymjPnt	vmcSource	VMCv20130304	Point source colour Y-J (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.
ymjPnt	vmcSource	VMCv20130805	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20140428	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20140903	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20150309	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20151218	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20160311	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20160822	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20170109	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20170411	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20171101	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20180702	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20181120	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20191212	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20210708	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20230816	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20240226	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource	VMCv20250130	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vmcSource, vmcSynopticSource	VMCDR1	Point source colour Y-J (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.
ymjPnt	vvvPsfDophotZYJHKsSource	VVVDR4	Point source colour Y-J (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.
ymjPnt	vvvPsfDophotZYJHKsSource	VVVDR5	Point source colour Y-J (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.
ymjPnt	vvvSource	VVVDR2	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vvvSource	VVVDR4	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vvvSource	VVVDR5	Point source colour Y-J (using aperMag3)	real	4	mag	-0.9999995e9	phot.color;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPnt	vvvSource	VVVv20100531	Point source colour Y-J (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.
ymjPnt	vvvSource	VVVv20110718	Point source colour Y-J (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.
ymjPnt	vvvSource, vvvSynopticSource	VVVDR1	Point source colour Y-J (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.
ymjPntErr	ultravistaSource	ULTRAVISTADR4	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on point source colour Y-J	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.
ymjPntErr	vhsSource	VHSDR1	Error on point source colour Y-J	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.
ymjPntErr	vhsSource	VHSDR2	Error on point source colour Y-J	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.
ymjPntErr	vhsSource	VHSDR3	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSDR4	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSDR5	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSDR6	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSDR7	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20120926	Error on point source colour Y-J	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.
ymjPntErr	vhsSource	VHSv20130417	Error on point source colour Y-J	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.
ymjPntErr	vhsSource	VHSv20140409	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20150108	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20160114	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20160507	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20170630	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20180419	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20201209	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vhsSource	VHSv20231101	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	videoSource	VIDEODR2	Error on point source colour Y-J	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.
ymjPntErr	videoSource	VIDEODR3	Error on point source colour Y-J	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.
ymjPntErr	videoSource	VIDEODR4	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	videoSource	VIDEODR5	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	videoSource	VIDEOv20100513	Error on point source colour Y-J	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.
ymjPntErr	videoSource	VIDEOv20111208	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGDR2	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGDR3	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGDR4	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vikingSource	VIKINGv20110714	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGv20111019	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGv20130417	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGv20140402	Error on point source colour Y-J	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.
ymjPntErr	vikingSource	VIKINGv20150421	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vikingSource	VIKINGv20151230	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vikingSource	VIKINGv20160406	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vikingSource	VIKINGv20161202	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vikingSource	VIKINGv20170715	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on point source colour Y-J	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.
ymjPntErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCDR2	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCDR3	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCDR4	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCDR5	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20110816	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCv20110909	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCv20120126	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCv20121128	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCv20130304	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCv20130805	Error on point source colour Y-J	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.
ymjPntErr	vmcSource	VMCv20140428	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20140903	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20150309	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20151218	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20160311	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20160822	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20170109	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20170411	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20171101	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20180702	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20181120	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20191212	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20210708	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20230816	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20240226	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource	VMCv20250130	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vmcSource, vmcSynopticSource	VMCDR1	Error on point source colour Y-J	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.
ymjPntErr	vvvPsfDophotZYJHKsSource	VVVDR4	Error on point source colour Y-J	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.
ymjPntErr	vvvPsfDophotZYJHKsSource	VVVDR5	Error on point source colour Y-J	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.
ymjPntErr	vvvSource	VVVDR2	Error on point source colour Y-J	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.
ymjPntErr	vvvSource	VVVDR4	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vvvSource	VVVDR5	Error on point source colour Y-J	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
			Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
ymjPntErr	vvvSource	VVVv20100531	Error on point source colour Y-J	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.
ymjPntErr	vvvSource	VVVv20110718	Error on point source colour Y-J	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.
ymjPntErr	vvvSource, vvvSynopticSource	VVVDR1	Error on point source colour Y-J	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.
ymjPntJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source colour calibrated flux J/Y (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.
ymjPntJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source colour calibrated flux J/Y (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.
ymjPntJky	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source colour calibrated flux J/Y (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.
ymjPntJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on point source colour calibrated flux J/Y	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.
ymjPntJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on point source colour calibrated flux J/Y	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.
ymjPntJkyErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on point source colour calibrated flux J/Y	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.
ymjPntLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source colour luptitude Y-J (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.
ymjPntLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source colour luptitude Y-J (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.
ymjPntLup	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source colour luptitude Y-J (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.
ymjPntLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error on point source colour luptitude Y-J	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.
ymjPntLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error on point source colour luptitude Y-J	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.
ymjPntLupErr	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error on point source colour luptitude Y-J	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.
ymjPsf	vmcPsfCatalogue	VMCDR4	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20150309	Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20151218	Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20160311	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20160822	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20170109	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20170411	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfCatalogue	VMCv20171101	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCDR5	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20180702	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20181120	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20191212	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20210708	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20230816	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20240226	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsf	vmcPsfSource	VMCv20250130	Y-J 3 pixels PSF fitting colour {catalogue TType keyword: YJ}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCDR4	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20150309	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20151218	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20160311	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20160822	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20170109	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20170411	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfCatalogue	VMCv20171101	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCDR5	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20180702	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20181120	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20191212	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20210708	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20230816	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20240226	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymjPsfErr	vmcPsfSource	VMCv20250130	Error on Y-J 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.J
ymksPsf	vmcPsfCatalogue	VMCDR4	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfCatalogue	VMCv20160311	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfCatalogue	VMCv20160822	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfCatalogue	VMCv20170109	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfCatalogue	VMCv20170411	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfCatalogue	VMCv20171101	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCDR5	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20180702	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20181120	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20191212	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20210708	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20230816	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20240226	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsf	vmcPsfSource	VMCv20250130	Y-Ks 3 pixels PSF fitting colour {catalogue TType keyword: YKs}	real	4	mag	-0.9999995e9	stat.fit.param;phot.color;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfCatalogue	VMCDR4	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfCatalogue	VMCv20160311	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfCatalogue	VMCv20160822	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfCatalogue	VMCv20170109	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfCatalogue	VMCv20170411	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfCatalogue	VMCv20171101	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCDR5	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20180702	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20181120	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20191212	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20210708	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20230816	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20240226	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymksPsfErr	vmcPsfSource	VMCv20250130	Error on Y-Ks 3 pixels PSF fitting colour	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;em.IR.K
ymomentR1	StackObjectAttributes	PS1DR2	First radial moment for y filter stack detection.	real	4	arcsec	-999
ymomentRH	StackObjectAttributes	PS1DR2	Half radial moment (r^0.5 weighting) for y filter stack detection.	real	4	arcsec^0.5	-999
ymomentXX	StackObjectAttributes	PS1DR2	Second moment M_xx for y filter stack detection.	real	4	arcsec^2	-999
ymomentXY	StackObjectAttributes	PS1DR2	Second moment M_xy for y filter stack detection.	real	4	arcsec^2	-999
ymomentYY	StackObjectAttributes	PS1DR2	Second moment M_yy for y filter stack detection.	real	4	arcsec^2	-999
yndof	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.
yndof	ultravistaVariability	ULTRAVISTADR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	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.
yndof	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.
yndof	videoVariability	VIDEODR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	videoVariability	VIDEODR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	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.
yndof	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.
yndof	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.
yndof	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.
yndof	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.
yndof	vmcVariability	VMCDR3	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCDR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCDR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	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.
yndof	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.
yndof	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.
yndof	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.
yndof	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.
yndof	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.
yndof	vmcVariability	VMCv20140428	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20140903	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20150309	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20151218	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20160311	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20160822	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20170109	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20170411	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20171101	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20180702	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20181120	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20191212	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20210708	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20230816	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20240226	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vmcVariability	VMCv20250130	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
yndof	vvvVariability	VVVDR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;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.
ynDofAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	videoVarFrameSetInfo	VIDEODR2	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	videoVarFrameSetInfo	VIDEODR3	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	videoVarFrameSetInfo	VIDEODR4	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	videoVarFrameSetInfo	VIDEODR5	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	videoVarFrameSetInfo	VIDEOv20100513	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	videoVarFrameSetInfo	VIDEOv20111208	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vikingVarFrameSetInfo	VIKINGv20110714	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCDR1	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCDR2	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCDR3	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCDR4	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCDR5	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20110816	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20110909	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20120126	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20121128	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20130304	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20130805	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20140428	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20140903	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20150309	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20151218	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20160311	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20160822	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20170109	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20170411	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20171101	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20180702	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20181120	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20191212	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20210708	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20230816	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20240226	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vmcVarFrameSetInfo	VMCv20250130	Number of degrees of freedom of astrometric fit in Y 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.
ynDofAst	vvvVarFrameSetInfo	VVVDR5	Number of degrees of freedom of astrometric fit in Y 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.
ynDofPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	videoVarFrameSetInfo	VIDEODR2	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	videoVarFrameSetInfo	VIDEODR3	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	videoVarFrameSetInfo	VIDEODR4	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	videoVarFrameSetInfo	VIDEODR5	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	videoVarFrameSetInfo	VIDEOv20100513	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	videoVarFrameSetInfo	VIDEOv20111208	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vikingVarFrameSetInfo	VIKINGv20110714	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCDR1	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCDR2	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCDR3	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCDR4	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCDR5	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20110816	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20110909	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20120126	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20121128	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20130304	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20130805	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20140428	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20140903	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20150309	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20151218	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20160311	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20160822	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20170109	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20170411	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20171101	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20180702	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20181120	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20191212	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20210708	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20230816	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20240226	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vmcVarFrameSetInfo	VMCv20250130	Number of degrees of freedom of photometric fit in Y 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.
ynDofPht	vvvVarFrameSetInfo	VVVDR5	Number of degrees of freedom of photometric fit in Y 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.
yNepochs	vmcCepheidVariables	VMCDR4	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20160311	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20160822	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20170109	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20170411	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20171101	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20180702	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20181120	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20191212	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20210708	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20230816	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
yNepochs	vmcCepheidVariables	VMCv20240226	Number of Ymag epochs {catalogue TType keyword: o_Ymag}	int	4		-99999999	meta.number;em.IR.NIR
ynFlaggedObs	ultravistaVariability	ULTRAVISTADR4	Number of detections in Y band flagged as potentially spurious by ultravistaDetection.ppErrBits	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.
ynFlaggedObs	videoVariability	VIDEODR2	Number of detections in Y 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.
ynFlaggedObs	videoVariability	VIDEODR3	Number of detections in Y 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.
ynFlaggedObs	videoVariability	VIDEODR4	Number of detections in Y band flagged as potentially spurious by videoDetection.ppErrBits	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.
ynFlaggedObs	videoVariability	VIDEODR5	Number of detections in Y band flagged as potentially spurious by videoDetection.ppErrBits	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.
ynFlaggedObs	videoVariability	VIDEOv20100513	Number of detections in Y 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.
ynFlaggedObs	videoVariability	VIDEOv20111208	Number of detections in Y 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.
ynFlaggedObs	vikingVariability	VIKINGv20110714	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCDR1	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCDR2	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCDR3	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCDR4	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCDR5	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20110816	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCv20110909	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCv20120126	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCv20121128	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCv20130304	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCv20130805	Number of detections in Y 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.
ynFlaggedObs	vmcVariability	VMCv20140428	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20140903	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20150309	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20151218	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20160311	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20160822	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20170109	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20170411	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20171101	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20180702	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20181120	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20191212	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20210708	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20230816	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20240226	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vmcVariability	VMCv20250130	Number of detections in Y band flagged as potentially spurious by vmcDetection.ppErrBits	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.
ynFlaggedObs	vvvVariability	VVVDR5	Number of detections in Y band flagged as potentially spurious by vvvDetection.ppErrBits	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.
ynFrames	StackObjectThin	PS1DR2	Number of input frames/exposures contributing to the y filter stack detection.	int	4		-999
ynGoodObs	ultravistaVariability	ULTRAVISTADR4	Number of good detections in Y 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.
ynGoodObs	videoVariability	VIDEODR2	Number of good detections in Y 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.
ynGoodObs	videoVariability	VIDEODR3	Number of good detections in Y 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.
ynGoodObs	videoVariability	VIDEODR4	Number of good detections in Y 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.
ynGoodObs	videoVariability	VIDEODR5	Number of good detections in Y 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.
ynGoodObs	videoVariability	VIDEOv20100513	Number of good detections in Y 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.
ynGoodObs	videoVariability	VIDEOv20111208	Number of good detections in Y 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.
ynGoodObs	vikingVariability	VIKINGv20110714	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCDR1	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCDR2	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCDR3	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCDR4	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCDR5	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20110816	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20110909	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20120126	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20121128	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20130304	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20130805	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20140428	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20140903	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20150309	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20151218	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20160311	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20160822	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20170109	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20170411	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20171101	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20180702	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20181120	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20191212	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20210708	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20230816	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20240226	Number of good detections in Y 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.
ynGoodObs	vmcVariability	VMCv20250130	Number of good detections in Y 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.
ynGoodObs	vvvVariability	VVVDR5	Number of good detections in Y 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.
yNgt3sig	ultravistaMapLcVariability	ULTRAVISTADR4	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	ultravistaVariability	ULTRAVISTADR4	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	videoVariability	VIDEODR2	Number of good detections in Y-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.
yNgt3sig	videoVariability	VIDEODR3	Number of good detections in Y-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.
yNgt3sig	videoVariability	VIDEODR4	Number of good detections in Y-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.
yNgt3sig	videoVariability	VIDEODR5	Number of good detections in Y-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.
yNgt3sig	videoVariability	VIDEOv20100513	Number of good detections in Y-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.
yNgt3sig	videoVariability	VIDEOv20111208	Number of good detections in Y-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.
yNgt3sig	vikingVariability	VIKINGv20110714	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCDR1	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCDR2	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCDR3	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCDR4	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCDR5	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20110816	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20110909	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20120126	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20121128	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20130304	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20130805	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20140428	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20140903	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20150309	Number of good detections in Y-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.
yNgt3sig	vmcVariability	VMCv20151218	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20160311	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20160822	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20170109	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20170411	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20171101	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20180702	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20181120	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20191212	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20210708	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20230816	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20240226	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vmcVariability	VMCv20250130	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
yNgt3sig	vvvVariability	VVVDR5	Number of good detections in Y-band that are more than 3 sigma deviations (yAperMagN < (yMeanMag-3*yMagRms)	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.
Ynmag	decapsSource	DECAPS	Number of detections in Y band {catalogue TType keyword: nmag[5]}	smallint	2			meta.number;em.IR.J
Ynmag_lbs	decapsSource	DECAPS	Number of detections in Y band {catalogue TType keyword: nmag_lbs[5]}	smallint	2		-9999	meta.number;em.IR.J
Ynmag_lbs_ok	decapsSource	DECAPS	Number of good detections in Y band {catalogue TType keyword: nmag_lbs_ok[5]}	smallint	2		-9999	meta.number;em.IR.J
Ynmag_ok	decapsSource	DECAPS	Number of good detections in Y band {catalogue TType keyword: nmag_ok[5]}	smallint	2			meta.number;em.IR.J
ynMissingObs	ultravistaVariability	ULTRAVISTADR4	Number of Y 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.
ynMissingObs	videoVariability	VIDEODR2	Number of Y 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.
ynMissingObs	videoVariability	VIDEODR3	Number of Y 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.
ynMissingObs	videoVariability	VIDEODR4	Number of Y 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.
ynMissingObs	videoVariability	VIDEODR5	Number of Y 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.
ynMissingObs	videoVariability	VIDEOv20100513	Number of Y 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.
ynMissingObs	videoVariability	VIDEOv20111208	Number of Y 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.
ynMissingObs	vikingVariability	VIKINGv20110714	Number of Y 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.
ynMissingObs	vmcVariability	VMCDR1	Number of Y 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.
ynMissingObs	vmcVariability	VMCDR2	Number of Y 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.
ynMissingObs	vmcVariability	VMCDR3	Number of Y 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.
ynMissingObs	vmcVariability	VMCDR4	Number of Y 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.
ynMissingObs	vmcVariability	VMCDR5	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20110816	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20110909	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20120126	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20121128	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20130304	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20130805	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20140428	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20140903	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20150309	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20151218	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20160311	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20160822	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20170109	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20170411	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20171101	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20180702	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20181120	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20191212	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20210708	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20230816	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20240226	Number of Y 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.
ynMissingObs	vmcVariability	VMCv20250130	Number of Y 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.
ynMissingObs	vvvVariability	VVVDR5	Number of Y 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.
ynNegFlagObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of flagged negative measurements in Y 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.
ynNegObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of unflagged negative measurements Y 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.
ynPosFlagObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of flagged positive measurements in Y 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.
ynPosObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of unflagged positive measurements in Y 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.
yOverlap	RequiredMosaicTopLevel	SHARKSv20210222	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	SHARKSv20210421	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	ULTRAVISTADR4	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VHSDR7	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VHSv20201209	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VHSv20231101	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCDEEPv20230713	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCDEEPv20240506	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCDR5	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCv20191212	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCv20210708	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCv20230816	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCv20240226	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VMCv20250130	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VVVDR5	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yOverlap	RequiredMosaicTopLevel	VVVXDR1	The overlap between adjacent mosaics in the y-direction	real	4	degrees		??
yPA	ultravistaSource	ULTRAVISTADR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	ultravistaSourceRemeasurement	ULTRAVISTADR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vhsSource	VHSDR2	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vhsSource	VHSDR3	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSDR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSDR5	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSDR6	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSDR7	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20120926	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vhsSource	VHSv20130417	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vhsSource	VHSv20140409	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20150108	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20160114	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20160507	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20170630	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20180419	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20201209	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource	VHSv20231101	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vhsSource, vhsSourceRemeasurement	VHSDR1	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	videoSource	VIDEODR2	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	videoSource	VIDEODR3	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	videoSource	VIDEODR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	videoSource	VIDEODR5	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	videoSource	VIDEOv20111208	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	videoSource, videoSourceRemeasurement	VIDEOv20100513	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingSource	VIKINGDR2	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingSource	VIKINGDR3	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingSource	VIKINGDR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vikingSource	VIKINGv20111019	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingSource	VIKINGv20130417	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingSource	VIKINGv20140402	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingSource	VIKINGv20150421	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vikingSource	VIKINGv20151230	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vikingSource	VIKINGv20160406	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vikingSource	VIKINGv20161202	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vikingSource	VIKINGv20170715	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCDR2	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCDR3	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCDR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCDR5	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20110909	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCv20120126	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCv20121128	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCv20130304	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCv20130805	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource	VMCv20140428	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20140903	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20150309	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20151218	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20160311	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20160822	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20170109	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20170411	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20171101	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20180702	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20181120	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20191212	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20210708	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20230816	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20240226	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource	VMCv20250130	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vmcSource, vmcSourceRemeasurement	VMCv20110816	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vmcSource, vmcSynopticSource	VMCDR1	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vvvSource	VVVDR2	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vvvSource	VVVDR4	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vvvSource	VVVDR5	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.NIR
yPA	vvvSource	VVVv20110718	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vvvSource, vvvSourceRemeasurement	VVVv20100531	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPA	vvvSource, vvvSynopticSource	VVVDR1	ellipse fit celestial orientation in Y	real	4	Degrees	-0.9999995e9	pos.posAng
yPetroJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y calibrated flux (Petrosian)	real	4	jansky	-0.9999995e9	phot.flux
yPetroJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Y calibrated flux (Petrosian)	real	4	jansky	-0.9999995e9	stat.error
yPetroLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y luptitude (Petrosian)	real	4	lup	-0.9999995e9	phot.lup
yPetroLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Y luptitude (Petrosian)	real	4	lup	-0.9999995e9	stat.error
yPetroMag	ultravistaSource	ULTRAVISTADR4	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Y magnitude (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vhsSource	VHSDR1	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vhsSource	VHSDR2	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vhsSource	VHSDR3	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSDR4	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSDR5	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSDR6	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSDR7	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20120926	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vhsSource	VHSv20130417	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vhsSource	VHSv20140409	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20150108	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20160114	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20160507	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20170630	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20180419	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20201209	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vhsSource	VHSv20231101	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	videoSource	VIDEODR2	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	videoSource	VIDEODR3	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	videoSource	VIDEODR4	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	videoSource	VIDEODR5	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	videoSource	VIDEOv20100513	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	videoSource	VIDEOv20111208	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vikingSource	VIKINGDR2	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vikingSource	VIKINGDR3	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vikingSource	VIKINGDR4	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vikingSource	VIKINGv20110714	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vikingSource	VIKINGv20111019	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vikingSource	VIKINGv20130417	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vikingSource	VIKINGv20140402	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vikingSource	VIKINGv20150421	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vikingSource	VIKINGv20151230	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vikingSource	VIKINGv20160406	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vikingSource	VIKINGv20161202	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vikingSource	VIKINGv20170715	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCDR1	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vmcSource	VMCDR2	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCDR3	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCDR4	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCDR5	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20110816	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vmcSource	VMCv20110909	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vmcSource	VMCv20120126	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vmcSource	VMCv20121128	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vmcSource	VMCv20130304	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
yPetroMag	vmcSource	VMCv20130805	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20140428	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20140903	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20150309	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20151218	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20160311	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20160822	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20170109	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20170411	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20171101	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20180702	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20181120	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20191212	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20210708	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20230816	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20240226	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMag	vmcSource	VMCv20250130	Extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPetroMagErr	ultravistaSource	ULTRAVISTADR4	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Y magnitude (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vhsSource	VHSDR1	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vhsSource	VHSDR2	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vhsSource	VHSDR3	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPetroMagErr	vhsSource	VHSDR4	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	vhsSource	VHSDR5	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSDR6	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSDR7	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20120926	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vhsSource	VHSv20130417	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vhsSource	VHSv20140409	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20150108	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	vhsSource	VHSv20160114	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20160507	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20170630	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20180419	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20201209	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vhsSource	VHSv20231101	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	videoSource	VIDEODR2	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	videoSource	VIDEODR3	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	videoSource	VIDEODR4	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	videoSource	VIDEODR5	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	videoSource	VIDEOv20100513	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	videoSource	VIDEOv20111208	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGDR2	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGDR3	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGDR4	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPetroMagErr	vikingSource	VIKINGv20110714	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGv20111019	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGv20130417	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGv20140402	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vikingSource	VIKINGv20150421	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	vikingSource	VIKINGv20151230	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vikingSource	VIKINGv20160406	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vikingSource	VIKINGv20161202	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vikingSource	VIKINGv20170715	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCDR1	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCDR2	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCDR3	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	vmcSource	VMCDR4	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCDR5	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20110816	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCv20110909	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCv20120126	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCv20121128	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCv20130304	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCv20130805	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
yPetroMagErr	vmcSource	VMCv20140428	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20140903	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	vmcSource	VMCv20150309	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPetroMagErr	vmcSource	VMCv20151218	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20160311	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20160822	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20170109	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20170411	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20171101	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20180702	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20181120	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20191212	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20210708	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20230816	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20240226	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPetroMagErr	vmcSource	VMCv20250130	Error in extended source Y mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
YPIXL15	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Pixel-Coordinate (y)	float	8	pix	999.999
YPIXL24	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Pixel-Coordinate (y)	float	8	pix	999.999
YPIXN3	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Pixel-Coordinate (y)	float	8	pix	999.999
YPIXS11	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Pixel-Coordinate (y)	float	8	pix	999.999
YPIXS7	akari_lmc_psa_v1, akari_lmc_psc_v1	AKARI	Pixel-Coordinate (y)	float	8	pix	999.999
yPixSize	CurrentAstrometry	SHARKSv20210222	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	SHARKSv20210421	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	ULTRAVISTADR4	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR1	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR2	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR3	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR4	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR5	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR6	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSDR7	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20120926	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20130417	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20140409	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20150108	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20160114	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20160507	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20170630	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20180419	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20201209	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VHSv20231101	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIDEODR2	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIDEODR3	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIDEODR4	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIDEODR5	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIDEOv20100513	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIDEOv20111208	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGDR2	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGDR3	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGDR4	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20110714	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20111019	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20130417	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20140402	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20150421	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20151230	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20160406	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20161202	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VIKINGv20170715	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDEEPv20230713	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDEEPv20240506	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDR1	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDR2	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDR3	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDR4	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCDR5	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20110816	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20110909	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20120126	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20121128	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20130304	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20130805	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20140428	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20140903	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20150309	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20151218	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20160311	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20160822	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20170109	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20170411	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20171101	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20180702	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20181120	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20191212	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20210708	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20230816	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20240226	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VMCv20250130	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVDR1	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVDR2	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVDR4	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVDR5	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVXDR1	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVv20100531	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	CurrentAstrometry	VVVv20110718	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPixSize	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	Angular size of pixels in Y	real	4	Arcseconds	-0.9999995e9	pos.angDistance
yPlateScale	StackObjectAttributes	PS1DR2	Local plate scale for the y filter stack.	real	4	arcsec/pixel	0
yPos	Detection	PS1DR2	PSF y center location.	real	4	raw pixels	-999
yPos	nvssSource	NVSS	Y position (Dec direction) of the radio source	real	4	pixels		pos.cartesian.y;instr.det
yPosErr	Detection	PS1DR2	Error in PSF y center location.	real	4	raw pixels	-999
yppErrBits	ultravistaSource	ULTRAVISTADR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
yppErrBits	ultravistaSourceRemeasurement	ULTRAVISTADR4	additional WFAU post-processing error bits in Y	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.
yppErrBits	vhsSource	VHSDR1	additional WFAU post-processing error bits in Y	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.
yppErrBits	vhsSource	VHSDR2	additional WFAU post-processing error bits in Y	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.
yppErrBits	vhsSource	VHSDR3	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSDR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSDR5	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSDR6	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSDR7	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20120926	additional WFAU post-processing error bits in Y	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.
yppErrBits	vhsSource	VHSv20130417	additional WFAU post-processing error bits in Y	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.
yppErrBits	vhsSource	VHSv20140409	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20150108	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20160114	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20160507	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20170630	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20180419	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20201209	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSource	VHSv20231101	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vhsSourceRemeasurement	VHSDR1	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	videoSource	VIDEODR2	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	videoSource	VIDEODR3	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	videoSource	VIDEODR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
yppErrBits	videoSource	VIDEODR5	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
yppErrBits	videoSource	VIDEOv20111208	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	videoSource, videoSourceRemeasurement	VIDEOv20100513	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vikingSource	VIKINGDR2	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingSource	VIKINGDR3	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingSource	VIKINGDR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vikingSource	VIKINGv20110714	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingSource	VIKINGv20111019	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingSource	VIKINGv20130417	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingSource	VIKINGv20140402	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingSource	VIKINGv20150421	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vikingSource	VIKINGv20151230	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vikingSource	VIKINGv20160406	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vikingSource	VIKINGv20161202	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vikingSource	VIKINGv20170715	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vikingSourceRemeasurement	VIKINGv20110714	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vikingSourceRemeasurement	VIKINGv20111019	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	additional WFAU post-processing error bits in Y	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.
yppErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCDR2	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCDR3	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCDR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCDR5	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20110816	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCv20110909	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCv20120126	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCv20121128	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCv20130304	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCv20130805	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSource	VMCv20140428	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20140903	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20150309	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20151218	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20160311	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20160822	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20170109	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20170411	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20171101	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20180702	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20181120	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20191212	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20210708	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20230816	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20240226	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource	VMCv20250130	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yppErrBits	vmcSource, vmcSynopticSource	VMCDR1	additional WFAU post-processing error bits in Y	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.
yppErrBits	vmcSourceRemeasurement	VMCv20110816	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vmcSourceRemeasurement	VMCv20110909	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vvvSource	VVVDR1	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vvvSource	VVVDR2	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vvvSource	VVVDR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
yppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
yppErrBits	vvvSource	VVVv20110718	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vvvSource, vvvSourceRemeasurement	VVVv20100531	additional WFAU post-processing error bits in Y	int	4		0	meta.code
yppErrBits	vvvSynopticSource	VVVDR1	additional WFAU post-processing error bits in Y	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.
yppErrBits	vvvSynopticSource	VVVDR2	additional WFAU post-processing error bits in Y	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.
yppErrBits	vvvSynopticSource	VVVDR4	additional WFAU post-processing error bits in Y	int	4		0	meta.code;em.IR.NIR
			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.
yprobVar	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.
yprobVar	ultravistaVariability	ULTRAVISTADR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	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.
yprobVar	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.
yprobVar	videoVariability	VIDEODR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	videoVariability	VIDEODR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	vmcVariability	VMCDR3	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCDR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCDR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	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.
yprobVar	vmcVariability	VMCv20140428	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20140903	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20150309	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20151218	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20160311	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20160822	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20170109	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20170411	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20171101	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20180702	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20181120	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20191212	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20210708	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20230816	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20240226	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vmcVariability	VMCv20250130	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
yprobVar	vvvVariability	VVVDR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;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.
ypsfChiSq	StackObjectAttributes	PS1DR2	Reduced chi squared value of the PSF model fit for y filter stack detection.	real	4		-999
ypsfCore	StackObjectAttributes	PS1DR2	PSF core parameter k from y filter stack detection, where F = F0 / (1 + k r^2 + r^3.33).	real	4		-999
yPSFFlux	StackObjectAttributes	PS1DR2	PSF flux from y filter stack detection.	real	4	Janskys	-999
yPSFFluxErr	StackObjectAttributes	PS1DR2	Error in PSF flux from y filter stack detection.	real	4	Janskys	-999
ypsfLikelihood	StackObjectAttributes	PS1DR2	Likelihood that this y filter stack detection is best fit by a PSF.	real	4		-999
yPSFMag	StackObjectThin	PS1DR2	PSF magnitude from y filter stack detection.	real	4	AB magnitudes	-999
yPsfMag	vhsSource	VHSDR1	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vhsSource	VHSDR2	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vhsSource	VHSDR3	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSDR4	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSDR5	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSDR6	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSDR7	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20120926	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vhsSource	VHSv20130417	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vhsSource	VHSv20140409	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20150108	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20160114	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20160507	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20170630	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20180419	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20201209	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vhsSource	VHSv20231101	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vikingSource	VIKINGDR2	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vikingSource	VIKINGDR3	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vikingSource	VIKINGDR4	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vikingSource	VIKINGv20110714	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vikingSource	VIKINGv20111019	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vikingSource	VIKINGv20130417	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vikingSource	VIKINGv20140402	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vikingSource	VIKINGv20150421	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vikingSource	VIKINGv20151230	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vikingSource	VIKINGv20160406	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vikingSource	VIKINGv20161202	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vikingSource	VIKINGv20170715	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCDR3	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCDR4	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20121128	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param
yPsfMag	vmcPsfCatalogue	VMCv20140428	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20140903	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20150309	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20151218	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20160311	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20160822	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20170109	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20170411	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfCatalogue	VMCv20171101	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfDetections	VMCv20180702	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfDetections	VMCv20181120	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR
yPsfMag	vmcPsfSource	VMCDR5	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20180702	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20181120	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20191212	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20210708	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20230816	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20240226	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcPsfSource	VMCv20250130	3 pixels PSF fitting magnitude Y filter {catalogue TType keyword: Y_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.NIR;meta.main
yPsfMag	vmcSource	VMCDR1	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vmcSource	VMCDR2	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCDR3	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCDR4	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCDR5	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20110816	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vmcSource	VMCv20110909	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vmcSource	VMCv20120126	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vmcSource	VMCv20121128	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vmcSource	VMCv20130304	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag
yPsfMag	vmcSource	VMCv20130805	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20140428	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20140903	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20150309	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20151218	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20160311	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20160822	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20170109	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20170411	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20171101	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20180702	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20181120	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20191212	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20210708	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20230816	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20240226	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vmcSource	VMCv20250130	Point source profile-fitted Y mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
yPsfMag	vvvPsfDophotZYJHKsSource	VVVDR4	Mean PSF magnitude in Y band {catalogue TType keyword: mag_Y}	real	4	mag	-0.9999995e9	instr.det.psf;phot.mag;em.IR.NIR;meta.main
yPsfMag	vvvPsfDophotZYJHKsSource	VVVDR5	Mean PSF magnitude in Y band {catalogue TType keyword: mag_Y}	real	4	mag	-0.9999995e9	instr.det.psf;phot.mag;em.IR.NIR;meta.main
yPSFMagErr	StackObjectThin	PS1DR2	Error in PSF magnitude from y filter stack detection.	real	4	AB magnitudes	-999
yPsfMagErr	vhsSource	VHSDR1	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vhsSource	VHSDR2	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vhsSource	VHSDR3	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPsfMagErr	vhsSource	VHSDR4	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPsfMagErr	vhsSource	VHSDR5	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSDR6	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSDR7	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20120926	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vhsSource	VHSv20130417	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vhsSource	VHSv20140409	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20150108	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPsfMagErr	vhsSource	VHSv20160114	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20160507	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20170630	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20180419	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20201209	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vhsSource	VHSv20231101	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGDR2	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGDR3	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGDR4	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPsfMagErr	vikingSource	VIKINGv20110714	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGv20111019	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGv20130417	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGv20140402	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vikingSource	VIKINGv20150421	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPsfMagErr	vikingSource	VIKINGv20151230	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vikingSource	VIKINGv20160406	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vikingSource	VIKINGv20161202	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vikingSource	VIKINGv20170715	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCDR3	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCDR4	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20121128	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcPsfCatalogue	VMCv20140428	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20140903	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20150309	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20151218	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20160311	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20160822	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20170109	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20170411	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfCatalogue	VMCv20171101	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfDetections	VMCv20181120	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcPsfDetections, vmcPsfSource	VMCv20180702	PSF error Y filter {catalogue TType keyword: Y_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCDR1	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCDR2	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCDR3	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPsfMagErr	vmcSource	VMCDR4	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCDR5	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20110816	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCv20110909	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCv20120126	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCv20121128	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCv20130304	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCv20130805	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error
yPsfMagErr	vmcSource	VMCv20140428	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20140903	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPsfMagErr	vmcSource	VMCv20150309	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
yPsfMagErr	vmcSource	VMCv20151218	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20160311	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20160822	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20170109	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20170411	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20171101	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20180702	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20181120	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20191212	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20210708	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20230816	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20240226	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vmcSource	VMCv20250130	Error in point source profile-fitted Y mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
yPsfMagErr	vvvPsfDophotZYJHKsSource	VVVDR4	Error on mean PSF magnitude in Y band {catalogue TType keyword: er_Y}	real	4	mag	-0.9999995e9	stat.error;instr.det.psf;em.IR.NIR
yPsfMagErr	vvvPsfDophotZYJHKsSource	VVVDR5	Error on mean PSF magnitude in Y band {catalogue TType keyword: er_Y}	real	4	mag	-0.9999995e9	stat.error;instr.det.psf;em.IR.NIR
ypsfMajorFWHM	StackObjectAttributes	PS1DR2	PSF major axis FWHM from y filter stack detection.	real	4	arcsec	-999
ypsfMinorFWHM	StackObjectAttributes	PS1DR2	PSF minor axis FWHM from y filter stack detection.	real	4	arcsec	-999
ypsfQf	StackObjectAttributes	PS1DR2	PSF coverage factor for y filter stack detection.	real	4		-999
ypsfQfPerfect	StackObjectAttributes	PS1DR2	PSF-weighted fraction of pixels totally unmasked for y filter stack detection.	real	4		-999
ypsfTheta	StackObjectAttributes	PS1DR2	PSF major axis orientation from y filter stack detection.	real	4	degrees	-999
Yq25	decapsSource	DECAPS	25th percentile Y-band flux (3631 Jy) {catalogue TType keyword: q25[5]}	real	4	3631Jy	-9.999995e8	stat.value;phot.flux;em.IR.J
Yq25_lbs	decapsSource	DECAPS	25th percentile local background subtracted Y-band flux (3631 Jy) {catalogue TType keyword: q25_lbs[5]}	real	4	3631Jy	-9.999995e8	stat.value;phot.flux;em.IR.J
Yq75	decapsSource	DECAPS	75th percentile Y-band flux (3631 Jy) {catalogue TType keyword: q75[5]}	real	4	3631Jy	-9.999995e8	stat.value;phot.flux;em.IR.J
Yq75_lbs	decapsSource	DECAPS	75th percentile local background subtracted Y-band flux (3631 Jy) {catalogue TType keyword: q75_lbs[5]}	real	4	3631Jy	-9.999995e8	stat.value;phot.flux;em.IR.J
yQfPerfect	MeanObject	PS1DR2	Maximum PSF weighted fraction of pixels totally unmasked from y filter detections.	real	4		-999
YR	eros2LMCSource, eros2SMCSource, erosLMCSource, erosSMCSource	EROS	Y pixel coordinate on red reference images relative to rebined reference images in [klmn] frame	real	4
yra	StackObjectThin	PS1DR2	Right ascension from y filter stack detection.	float	8	degrees	-999
yraErr	StackObjectThin	PS1DR2	Right ascension error from y filter stack detection.	real	4	arcsec	-999
yref	smashdr2_source	SMASH	Y-coordinate for this source in the reference chip (1-indexed)	real	4
Ysep	vvvProperMotionCatalogue	VVVDR4	Sky distance between VVV DR4 Y detection and the projected source position at the Y observation epoch taking the pipeline proper motion into account. {catalogue TType keyword: Ysep}	real	4	arcsec	-999999500.0
Ysep	vvvProperMotionCatalogue	VVVDR5	Sky distance between VVV DR4 Y detection and the projected source position at the Y observation epoch taking the pipeline proper motion into account. {catalogue TType keyword: Ysep}	real	4	arcsec	-999999500.0
ySeqNum	ultravistaSource	ULTRAVISTADR4	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSDR1	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vhsSource	VHSDR2	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vhsSource	VHSDR3	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSDR4	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSDR5	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSDR6	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSDR7	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vhsSource	VHSv20120926	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vhsSource	VHSv20130417	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vhsSource	VHSv20140409	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSv20150108	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSv20160114	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSv20160507	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSv20170630	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSv20180419	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vhsSource	VHSv20201209	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vhsSource	VHSv20231101	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vhsSourceRemeasurement	VHSDR1	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	videoSource	VIDEODR2	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	videoSource	VIDEODR3	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	videoSource	VIDEODR4	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	videoSource	VIDEODR5	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	videoSource	VIDEOv20100513	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	videoSource	VIDEOv20111208	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	videoSourceRemeasurement	VIDEOv20100513	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	vikingSource	VIKINGDR2	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vikingSource	VIKINGDR3	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vikingSource	VIKINGDR4	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vikingSource	VIKINGv20110714	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vikingSource	VIKINGv20111019	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vikingSource	VIKINGv20130417	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vikingSource	VIKINGv20140402	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vikingSource	VIKINGv20150421	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vikingSource	VIKINGv20151230	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vikingSource	VIKINGv20160406	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vikingSource	VIKINGv20161202	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vikingSource	VIKINGv20170715	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vikingSourceRemeasurement	VIKINGv20110714	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	vikingSourceRemeasurement	VIKINGv20111019	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	vmcSource	VMCDR2	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vmcSource	VMCDR3	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCDR4	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCDR5	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vmcSource	VMCv20110816	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vmcSource	VMCv20110909	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vmcSource	VMCv20120126	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vmcSource	VMCv20121128	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vmcSource	VMCv20130304	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vmcSource	VMCv20130805	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vmcSource	VMCv20140428	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20140903	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20150309	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20151218	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20160311	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20160822	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20170109	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20170411	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20171101	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20180702	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20181120	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vmcSource	VMCv20191212	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vmcSource	VMCv20210708	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vmcSource	VMCv20230816	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vmcSource	VMCv20240226	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vmcSource	VMCv20250130	the running number of the Y detection	int	4		-99999999	meta.id;em.IR.NIR
ySeqNum	vmcSource, vmcSynopticSource	VMCDR1	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vmcSourceRemeasurement	VMCv20110816	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	vmcSourceRemeasurement	VMCv20110909	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	vvvSource	VVVDR2	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vvvSource	VVVDR4	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vvvSource	VVVDR5	the running number of the Y detection	int	4		-99999999	meta.number;em.IR.NIR
ySeqNum	vvvSource	VVVv20100531	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vvvSource	VVVv20110718	the running number of the Y detection	int	4		-99999999	meta.id
ySeqNum	vvvSource, vvvSynopticSource	VVVDR1	the running number of the Y detection	int	4		-99999999	meta.number
ySeqNum	vvvSourceRemeasurement	VVVv20100531	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySeqNum	vvvSourceRemeasurement	VVVv20110718	the running number of the Y remeasurement	int	4		-99999999	meta.id
ySerMag2D	vhsSource	VHSDR1	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vhsSource	VHSDR2	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vhsSource	VHSDR3	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSDR4	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSDR5	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSDR6	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSDR7	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20120926	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vhsSource	VHSv20130417	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vhsSource	VHSv20140409	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20150108	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20160114	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20160507	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20170630	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20180419	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20201209	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vhsSource	VHSv20231101	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vikingSource	VIKINGDR2	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vikingSource	VIKINGDR3	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vikingSource	VIKINGDR4	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vikingSource	VIKINGv20110714	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vikingSource	VIKINGv20111019	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vikingSource	VIKINGv20130417	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vikingSource	VIKINGv20140402	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vikingSource	VIKINGv20150421	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vikingSource	VIKINGv20151230	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vikingSource	VIKINGv20160406	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vikingSource	VIKINGv20161202	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vikingSource	VIKINGv20170715	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCDR1	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vmcSource	VMCDR2	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCDR3	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCDR4	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCDR5	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20110816	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vmcSource	VMCv20110909	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vmcSource	VMCv20120126	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vmcSource	VMCv20121128	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vmcSource	VMCv20130304	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ySerMag2D	vmcSource	VMCv20130805	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20140428	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20140903	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20150309	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20151218	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20160311	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20160822	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20170109	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20170411	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20171101	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20180702	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20181120	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20191212	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20210708	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20230816	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20240226	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2D	vmcSource	VMCv20250130	Extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSDR1	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vhsSource	VHSDR2	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vhsSource	VHSDR3	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
ySerMag2DErr	vhsSource	VHSDR4	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
ySerMag2DErr	vhsSource	VHSDR5	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSDR6	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSDR7	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20120926	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vhsSource	VHSv20130417	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vhsSource	VHSv20140409	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20150108	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
ySerMag2DErr	vhsSource	VHSv20160114	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20160507	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20170630	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20180419	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20201209	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vhsSource	VHSv20231101	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGDR2	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGDR3	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGDR4	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
ySerMag2DErr	vikingSource	VIKINGv20110714	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGv20111019	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGv20130417	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGv20140402	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vikingSource	VIKINGv20150421	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
ySerMag2DErr	vikingSource	VIKINGv20151230	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vikingSource	VIKINGv20160406	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vikingSource	VIKINGv20161202	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vikingSource	VIKINGv20170715	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCDR1	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCDR2	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCDR3	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
ySerMag2DErr	vmcSource	VMCDR4	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCDR5	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20110816	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCv20110909	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCv20120126	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCv20121128	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCv20130304	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCv20130805	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ySerMag2DErr	vmcSource	VMCv20140428	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20140903	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
ySerMag2DErr	vmcSource	VMCv20150309	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR;phot.mag
ySerMag2DErr	vmcSource	VMCv20151218	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20160311	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20160822	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20170109	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20170411	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20171101	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20180702	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20181120	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20191212	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20210708	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20230816	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20240226	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySerMag2DErr	vmcSource	VMCv20250130	Error in extended source Y mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCDR3	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCDR4	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20121128	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param
ySharp	vmcPsfCatalogue	VMCv20140428	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20140903	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20150309	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20151218	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20160311	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20160822	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20170109	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20170411	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfCatalogue	VMCv20171101	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfDetections	VMCv20181120	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySharp	vmcPsfDetections, vmcPsfSource	VMCv20180702	PSF fitting shape parameter Y filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Y_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.NIR
ySize	MultiframeDetector	SHARKSv20210222	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	SHARKSv20210421	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	ULTRAVISTADR4	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR1	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR2	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR3	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR4	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR5	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR6	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSDR7	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20120926	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20130417	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20140409	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20150108	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20160114	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20160507	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20170630	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20180419	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20201209	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VHSv20231101	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIDEODR2	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIDEODR3	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIDEODR4	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIDEODR5	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIDEOv20100513	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIDEOv20111208	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGDR2	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGDR3	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGDR4	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20110714	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20111019	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20130417	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20140402	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20150421	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20151230	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20160406	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20161202	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VIKINGv20170715	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDEEPv20230713	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDEEPv20240506	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDR1	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDR2	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDR3	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDR4	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCDR5	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20110816	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20110909	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20120126	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20121128	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20130304	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20130805	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20140428	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20140903	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20150309	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20151218	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20160311	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20160822	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20170109	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20170411	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20171101	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20180702	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20181120	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20191212	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20210708	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20230816	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20240226	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VMCv20250130	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVDR1	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVDR2	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVDR4	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVDR5	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVXDR1	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVv20100531	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	MultiframeDetector	VVVv20110718	Y-axis size of image {catalogue extension keyword:  NYOUT}	int	4		-9999
ySize	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Y-axis size of image	int	4		-9999
yskewness	ultravistaMapLcVariability	ULTRAVISTADR4	Skewness in Y 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.
yskewness	ultravistaVariability	ULTRAVISTADR4	Skewness in Y 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.
yskewness	videoVariability	VIDEODR2	Skewness in Y 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.
yskewness	videoVariability	VIDEODR3	Skewness in Y 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.
yskewness	videoVariability	VIDEODR4	Skewness in Y 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.
yskewness	videoVariability	VIDEODR5	Skewness in Y 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.
yskewness	videoVariability	VIDEOv20100513	Skewness in Y 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.
yskewness	videoVariability	VIDEOv20111208	Skewness in Y 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.
yskewness	vikingVariability	VIKINGv20110714	Skewness in Y 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.
yskewness	vmcVariability	VMCDR1	Skewness in Y 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.
yskewness	vmcVariability	VMCDR2	Skewness in Y 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.
yskewness	vmcVariability	VMCDR3	Skewness in Y 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.
yskewness	vmcVariability	VMCDR4	Skewness in Y 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.
yskewness	vmcVariability	VMCDR5	Skewness in Y 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.
yskewness	vmcVariability	VMCv20110816	Skewness in Y 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.
yskewness	vmcVariability	VMCv20110909	Skewness in Y 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.
yskewness	vmcVariability	VMCv20120126	Skewness in Y 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.
yskewness	vmcVariability	VMCv20121128	Skewness in Y 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.
yskewness	vmcVariability	VMCv20130304	Skewness in Y 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.
yskewness	vmcVariability	VMCv20130805	Skewness in Y 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.
yskewness	vmcVariability	VMCv20140428	Skewness in Y 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.
yskewness	vmcVariability	VMCv20140903	Skewness in Y 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.
yskewness	vmcVariability	VMCv20150309	Skewness in Y 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.
yskewness	vmcVariability	VMCv20151218	Skewness in Y 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.
yskewness	vmcVariability	VMCv20160311	Skewness in Y 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.
yskewness	vmcVariability	VMCv20160822	Skewness in Y 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.
yskewness	vmcVariability	VMCv20170109	Skewness in Y 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.
yskewness	vmcVariability	VMCv20170411	Skewness in Y 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.
yskewness	vmcVariability	VMCv20171101	Skewness in Y 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.
yskewness	vmcVariability	VMCv20180702	Skewness in Y 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.
yskewness	vmcVariability	VMCv20181120	Skewness in Y 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.
yskewness	vmcVariability	VMCv20191212	Skewness in Y 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.
yskewness	vmcVariability	VMCv20210708	Skewness in Y 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.
yskewness	vmcVariability	VMCv20230816	Skewness in Y 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.
yskewness	vmcVariability	VMCv20240226	Skewness in Y 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.
yskewness	vmcVariability	VMCv20250130	Skewness in Y 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.
yskewness	vvvVariability	VVVDR5	Skewness in Y 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.
ysky	StackObjectAttributes	PS1DR2	Residual background sky level at the y filter stack detection.	real	4	Janskys/arcsec^2	-999
yskyErr	StackObjectAttributes	PS1DR2	Error in residual background sky level at the y filter stack detection.	real	4	Janskys/arcsec^2	-999
ystackDetectID	StackObjectAttributes, StackObjectThin	PS1DR2	Unique stack detection identifier.	bigint	8
ystackImageID	StackObjectAttributes, StackObjectThin	PS1DR2	Unique stack identifier for y filter detection.	bigint	8
Ystdev	decapsSource	DECAPS	Standard deviation in Y-band flux; may be default in rare cases of objects with 1 measurement {catalogue TType keyword: stdev[5]}	real	4	3631Jy	-9.999995e8	stat.stdev;phot.flux;em.IR.J
Ystdev_lbs	decapsSource	DECAPS	Standard deviation in local background subtracted Y-band fluxes; may be default in rare cases of objects with 1 measurement {catalogue TType keyword: stdev_lbs[5]}	real	4	3631Jy	-9.999995e8	stat.stdev;phot.flux;em.IR.J
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	vmcVariability	VMCv20140428	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration;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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	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.
ytotalPeriod	vmcVariability	VMCv20250130	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.
ytotalPeriod	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.
yVarClass	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.
yVarClass	ultravistaVariability	ULTRAVISTADR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	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.
yVarClass	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.
yVarClass	videoVariability	VIDEODR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	videoVariability	VIDEODR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	vmcVariability	VMCDR3	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCDR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCDR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	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.
yVarClass	vmcVariability	VMCv20140428	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20140903	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20150309	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20151218	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20160311	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20160822	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20170109	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20170411	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20171101	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20180702	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20181120	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20191212	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20210708	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20230816	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20240226	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vmcVariability	VMCv20250130	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yVarClass	vvvVariability	VVVDR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;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.
yXi	ultravistaSource	ULTRAVISTADR4	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSDR1	Offset of Y 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.
yXi	vhsSource	VHSDR2	Offset of Y 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.
yXi	vhsSource	VHSDR3	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSDR4	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSDR5	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSDR6	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSDR7	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20120926	Offset of Y 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.
yXi	vhsSource	VHSv20130417	Offset of Y 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.
yXi	vhsSource	VHSv20140409	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20150108	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20160114	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20160507	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20170630	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20180419	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20201209	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vhsSource	VHSv20231101	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	videoSource	VIDEODR2	Offset of Y 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.
yXi	videoSource	VIDEODR3	Offset of Y 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.
yXi	videoSource	VIDEODR4	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	videoSource	VIDEODR5	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	videoSource	VIDEOv20100513	Offset of Y 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.
yXi	videoSource	VIDEOv20111208	Offset of Y 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.
yXi	vikingSource	VIKINGDR2	Offset of Y 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.
yXi	vikingSource	VIKINGDR3	Offset of Y 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.
yXi	vikingSource	VIKINGDR4	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vikingSource	VIKINGv20110714	Offset of Y 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.
yXi	vikingSource	VIKINGv20111019	Offset of Y 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.
yXi	vikingSource	VIKINGv20130417	Offset of Y 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.
yXi	vikingSource	VIKINGv20140402	Offset of Y 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.
yXi	vikingSource	VIKINGv20150421	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vikingSource	VIKINGv20151230	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vikingSource	VIKINGv20160406	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vikingSource	VIKINGv20161202	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vikingSource	VIKINGv20170715	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCDR2	Offset of Y 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.
yXi	vmcSource	VMCDR3	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCDR4	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCDR5	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20110816	Offset of Y 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.
yXi	vmcSource	VMCv20110909	Offset of Y 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.
yXi	vmcSource	VMCv20120126	Offset of Y 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.
yXi	vmcSource	VMCv20121128	Offset of Y 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.
yXi	vmcSource	VMCv20130304	Offset of Y 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.
yXi	vmcSource	VMCv20130805	Offset of Y 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.
yXi	vmcSource	VMCv20140428	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20140903	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20150309	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20151218	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20160311	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20160822	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20170109	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20170411	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20171101	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20180702	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20181120	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20191212	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20210708	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20230816	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20240226	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource	VMCv20250130	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vmcSource, vmcSynopticSource	VMCDR1	Offset of Y 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.
yXi	vvvSource	VVVDR2	Offset of Y 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.
yXi	vvvSource	VVVDR4	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vvvSource	VVVDR5	Offset of Y detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.NIR
			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.
yXi	vvvSource	VVVv20100531	Offset of Y 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.
yXi	vvvSource	VVVv20110718	Offset of Y 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.
yXi	vvvSource, vvvSynopticSource	VVVDR1	Offset of Y 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.
yxPos	StackObjectAttributes	PS1DR2	PSF x center location from y filter stack detection.	real	4	sky pixels	-999
yxPosErr	StackObjectAttributes	PS1DR2	Error in PSF x center location from y filter stack detection.	real	4	sky pixels	-999
yyPos	StackObjectAttributes	PS1DR2	PSF y center location from y filter stack detection.	real	4	sky pixels	-999
yyPosErr	StackObjectAttributes	PS1DR2	Error in PSF y center location from y filter stack detection.	real	4	sky pixels	-999
yzp	StackObjectAttributes	PS1DR2	Photometric zeropoint for the y filter stack. Necessary for converting listed fluxes and magnitudes back to measured ADU counts.	real	4	magnitudes	0