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### Glossary of VSA attributes

##### This Glossary alphabetically lists all attributes used in the VSAv20150413 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20150413Schema 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

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified 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 svNgc253Detection SVNGC253v20100429 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 svOrionDetection SVORIONv20100429 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 ultravistaDetection ULTRAVISTAv20100429 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 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 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 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 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 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 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 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 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 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 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 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 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, 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 vvvListRemeasurement VVVv20110718 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_BJ grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS Plate y_bj in 8 micron pixels real 4
y_coadd twomass_xsc 2MASS 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
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 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 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 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 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 vvvSource VVVDR1 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
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 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 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 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 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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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 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 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 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 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 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
yAperMag3Err svNgc253Source SVNGC253v20100429 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err svOrionSource SVORIONv20100429 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err ultravistaSource ULTRAVISTAv20100429 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) 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 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 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 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 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 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, 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 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
yAperMag4 svNgc253Source SVNGC253v20100429 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 svOrionSource SVORIONv20100429 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 ultravistaSource ULTRAVISTAv20100429 Extended source Y mag, no aperture correction applied 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 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 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 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 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 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 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 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 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 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 svNgc253Source SVNGC253v20100429 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err svOrionSource SVORIONv20100429 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err ultravistaSource ULTRAVISTAv20100429 Error in extended source Y mag (2.8 arcsec aperture diameter) 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 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 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 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 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 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 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 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 vvvSource VVVDR2 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
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 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 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 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 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 svNgc253Source SVNGC253v20100429 Extended source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 svOrionSource SVORIONv20100429 Extended source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 ultravistaSource ULTRAVISTAv20100429 Extended source Y mag, no aperture correction applied 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 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 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 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 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 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
yAperMag6Err svNgc253Source SVNGC253v20100429 Error in extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err svOrionSource SVORIONv20100429 Error in extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err ultravistaSource ULTRAVISTAv20100429 Error in extended source Y mag (5.7 arcsec aperture diameter) 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 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 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 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 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 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
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 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 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 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 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 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
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 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 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 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 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 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
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 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 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 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 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 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
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 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 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.
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 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 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.
yAverageConf svNgc253Source SVNGC253v20100429 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf svOrionSource SVORIONv20100429 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
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 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 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 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 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, 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, 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 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 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 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)
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 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 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.
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 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 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.
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 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 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.
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 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 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.
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 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 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.
yClass svNgc253Source SVNGC253v20100429 discrete image classification flag in Y smallint 2   -9999 src.class
yClass svOrionSource SVORIONv20100429 discrete image classification flag in Y smallint 2   -9999 src.class
yClass ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 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 svNgc253Source SVNGC253v20100429 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat svOrionSource SVORIONv20100429 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat ultravistaSource ULTRAVISTAv20100429 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat
yClassStat ultravistaSourceRemeasurement ULTRAVISTAv20100429 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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 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 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 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 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.
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 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 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.
yDeblend ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
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
yEll svNgc253Source SVNGC253v20100429 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll svOrionSource SVORIONv20100429 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 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 svNgc253MergeLog SVNGC253v20100429 the extension number of this Y frame tinyint 1     meta.number
yeNum svOrionMergeLog SVORIONv20100429 the extension number of this Y frame tinyint 1     meta.number
yeNum ultravistaMergeLog ULTRAVISTAv20100429 the extension number of this Y frame tinyint 1     meta.number
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 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 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 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 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 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, 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 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
yErr svNgc253Detection SVNGC253v20100429 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr svOrionDetection SVORIONv20100429 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr ultravistaDetection ULTRAVISTAv20100429 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSDR1 Error in Y coordinate {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 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 videoDetection VIDEODR2 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEODR3 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEODR4 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEOv20100513 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEOv20111208 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr 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 VIKINGv20110714 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 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 VMCv20110816 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 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, vvvListRemeasurement VVVv20100531 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vvvListRemeasurement VVVv20110718 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
YERR_R spectra SIXDF error on Y_R position int 4
YERR_V spectra SIXDF error on Y_V position int 4
yErrBits svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 ultravistaSourceRemeasurement ULTRAVISTAv20100429 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
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 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 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 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 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 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 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, 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 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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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 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, 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 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 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 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 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
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 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 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.
yGausig svNgc253Source SVNGC253v20100429 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig svOrionSource SVORIONv20100429 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 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 videoSource VIDEODR4 SExtractor half-light radius in Y band real 4 pixels -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs svNgc253Source SVNGC253v20100429 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs ultravistaSource ULTRAVISTAv20100429 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
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 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 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 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
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 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 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.
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 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 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
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 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 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
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 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.
yKronMag videoSource VIDEODR4 Extended source Y mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yKronMagErr videoSource VIDEODR4 Extended source Y mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yMag ultravistaSourceRemeasurement ULTRAVISTAv20100429 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
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 ultravistaSourceRemeasurement ULTRAVISTAv20100429 Error in Y mag real 4 mag -0.9999995e9 stat.error
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 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
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 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 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.
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 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 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.
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 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 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.
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 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 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.
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 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 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.
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 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 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.
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 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 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.
ymfID svNgc253MergeLog SVNGC253v20100429 the UID of the relevant Y multiframe bigint 8     obs.field
ymfID svOrionMergeLog SVORIONv20100429 the UID of the relevant Y multiframe bigint 8     obs.field
ymfID ultravistaMergeLog ULTRAVISTAv20100429 the UID of the relevant Y multiframe bigint 8     obs.field
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 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 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 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 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 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, 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 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 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 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 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.
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 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 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.
ymj ultravistaSourceRemeasurement ULTRAVISTAv20100429 Default colour Y-J (using appropriate mags) real 4 mag   PHOT_COLOR
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
yMjd vmcSynopticSource VMCDR1 Modified Julian Day in Y band float 8 days -0.9999995e9 ??
yMjd vmcSynopticSource VMCDR2 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
yMjd vmcSynopticSource VMCDR3 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
yMjd vmcSynopticSource VMCv20110816 Modified Julian Day in Y band float 8 days -0.9999995e9 ??
yMjd vmcSynopticSource VMCv20110909 Modified Julian Day in Y band float 8 days -0.9999995e9 ??
yMjd vmcSynopticSource VMCv20120126 Modified Julian Day in Y band float 8 days -0.9999995e9 ??
yMjd vmcSynopticSource VMCv20121128 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
yMjd vmcSynopticSource VMCv20130304 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
yMjd vmcSynopticSource VMCv20130805 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
yMjd vmcSynopticSource VMCv20140428 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch;em.IR.NIR
yMjd vmcSynopticSource VMCv20140903 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
yMjd vmcSynopticSource VMCv20150309 Modified Julian Day in Y band float 8 days -0.9999995e9 time.epoch
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 ultravistaSourceRemeasurement ULTRAVISTAv20100429 Error on colour Y-J real 4 mag   stat.error
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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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 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 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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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 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.
ymjPnt svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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, 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 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 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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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, 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 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 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.
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
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
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 VIDEOv20100513