VSA Browser

Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20240329 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20240329 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 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, 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 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, 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 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 VVVDR5 the UID of the relevant 1st epoch Y tile multiframe bigint 8 meta.id;obs.field;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 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 VVVDR5 the UID of the relevant 2nd epoch Y tile multiframe bigint 8 meta.id;obs.field;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

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

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 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 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 vvvSource VVVDR1 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error

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 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

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 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

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 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 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 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 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 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 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, 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 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 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 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 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 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 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 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 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 vvvSource VVVDR2 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error

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 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

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 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

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 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

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 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

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 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

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 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

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 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

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 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 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 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, 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 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 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 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 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 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 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 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, vvvProperMotionCatalogue 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 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 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, 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 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 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, 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 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

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 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 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, vvvProperMotionCatalogue VVVDR5 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 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, 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 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 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, 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 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_IMAGE^½) {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 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_IMAGE^½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error. real 4 pixels stat.error

yErr videoDetection VIDEODR3 Error in Y coordinate (SE: ERRY2_IMAGE^½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error. real 4 pixels stat.error

yErr videoDetection VIDEODR4 Error in Y coordinate (SE: ERRY2_IMAGE^½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error. real 4 pixels stat.error

yErr videoDetection VIDEODR5 Error in Y coordinate (SE: ERRY2_IMAGE^½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error. real 4 pixels stat.error

yErr videoDetection VIDEOv20100513 Error in Y coordinate (SE: ERRY2_IMAGE^½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error. real 4 pixels stat.error

yErr videoDetection VIDEOv20111208 Error in Y coordinate (SE: ERRY2_IMAGE^½) {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_IMAGE^½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error. real 4 pixels stat.error

yErr vikingMapRemeasurement VIKINGZYSELJv20170124 Error in Y coordinate (SE: ERRY2_IMAGE^½) {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, 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 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, 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 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, 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 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 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, 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 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 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 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 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 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 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 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, 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 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 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 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 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 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.

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, vvvProperMotionCatalogue VVVDR5 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 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 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 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 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 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 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_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 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 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 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 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 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 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, 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 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 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 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 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, 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

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 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 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 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 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 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, 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 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 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 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, 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 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 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

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

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

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

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 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 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 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 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 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 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 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 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 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 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 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, 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 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 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

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 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

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 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 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 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 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 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, 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 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.

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 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 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 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 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 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 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 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 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, 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 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 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

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 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

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 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 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 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 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 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 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 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 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, 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 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

WFAU, Institute for Astronomy,
Royal Observatory, Blackford Hill
Edinburgh, EH9 3HJ, UK

vsa-support@roe.ac.uk
29/03/2024