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

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

K
Name	Schema Table	Database	Description	Type	Length	Unit	Default Value	Unified Content Descriptor
K	twomass	SIXDF	K magnitude (corrected) used for K selection	real	4	mag
k1	catwise_2020, catwise_prelim	WISE	W1 photometry usage code (0: neither the ascending nor the descending scan provided a solution; 1: only the ascending scan provided a solution; 2: only the descending scan provided a solution; 3: both scans provided solutions which were combined in the relevant way)	int	4
k2	catwise_2020, catwise_prelim	WISE	W2 photometry usage code (0: neither the ascending nor the descending scan provided a solution; 1: only the ascending scan provided a solution; 2: only the descending scan provided a solution; 3: both scans provided solutions which were combined in the relevant way)	int	4
k_2mrat	twomass_scn	TWOMASS	Ks-band average 2nd image moment ratio.	real	4			stat.fit.param
k_2mrat	twomass_sixx2_scn	TWOMASS	K band average 2nd image moment ratio for scan	real	4
k_5sig_ba	twomass_xsc	TWOMASS	K minor/major axis ratio fit to the 5-sigma isophote.	real	4			phys.size.axisRatio
k_5sig_phi	twomass_xsc	TWOMASS	K angle to 5-sigma major axis (E of N).	smallint	2	degrees		stat.error
k_5surf	twomass_xsc	TWOMASS	K central surface brightness (r<=5).	real	4	mag		phot.mag.sb
k_ba	twomass_sixx2_xsc	TWOMASS	K minor/major axis ratio fit to the 3-sigma isophote	real	4
k_ba	twomass_xsc	TWOMASS	K minor/major axis ratio fit to the 3-sigma isophote.	real	4			phys.size.axisRatio
k_back	twomass_xsc	TWOMASS	K coadd median background.	real	4			meta.code
k_bisym_chi	twomass_xsc	TWOMASS	K bi-symmetric cross-correlation chi.	real	4			stat.fit.param
k_bisym_rat	twomass_xsc	TWOMASS	K bi-symmetric flux ratio.	real	4			phot.flux;arith.ratio
k_bndg_amp	twomass_xsc	TWOMASS	K banding maximum FT amplitude on this side of coadd.	real	4	DN		stat.fit.param
k_bndg_per	twomass_xsc	TWOMASS	K banding Fourier Transf. period on this side of coadd.	int	4	arcsec		stat.fit.param
k_chif_ellf	twomass_xsc	TWOMASS	K % chi-fraction for elliptical fit to 3-sig isophote.	real	4			stat.fit.param
k_cmsig	twomass_psc	TWOMASS	Corrected photometric uncertainty for the default Ks-band magnitude.	real	4	mag	Ks-band	phot.flux
k_con_indx	twomass_xsc	TWOMASS	K concentration index r_75%/r_25%.	real	4			phys.size;arith.ratio
k_d_area	twomass_xsc	TWOMASS	K 5-sigma to 3-sigma differential area.	smallint	2			stat.fit.residual
k_flg_10	twomass_xsc	TWOMASS	K confusion flag for 10 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_15	twomass_xsc	TWOMASS	K confusion flag for 15 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_20	twomass_xsc	TWOMASS	K confusion flag for 20 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_25	twomass_xsc	TWOMASS	K confusion flag for 25 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_30	twomass_xsc	TWOMASS	K confusion flag for 30 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_40	twomass_xsc	TWOMASS	K confusion flag for 40 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_5	twomass_xsc	TWOMASS	K confusion flag for 5 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_50	twomass_xsc	TWOMASS	K confusion flag for 50 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_60	twomass_xsc	TWOMASS	K confusion flag for 60 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_7	twomass_sixx2_xsc	TWOMASS	K confusion flag for 7 arcsec circular ap. mag	smallint	2
k_flg_7	twomass_xsc	TWOMASS	K confusion flag for 7 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_70	twomass_xsc	TWOMASS	K confusion flag for 70 arcsec circular ap. mag.	smallint	2			meta.code
k_flg_c	twomass_xsc	TWOMASS	K confusion flag for Kron circular mag.	smallint	2			meta.code
k_flg_e	twomass_xsc	TWOMASS	K confusion flag for Kron elliptical mag.	smallint	2			meta.code
k_flg_fc	twomass_xsc	TWOMASS	K confusion flag for fiducial Kron circ. mag.	smallint	2			meta.code
k_flg_fe	twomass_xsc	TWOMASS	K confusion flag for fiducial Kron ell. mag.	smallint	2			meta.code
k_flg_i20c	twomass_xsc	TWOMASS	K confusion flag for 20mag/sq." iso. circ. mag.	smallint	2			meta.code
k_flg_i20e	twomass_xsc	TWOMASS	K confusion flag for 20mag/sq." iso. ell. mag.	smallint	2			meta.code
k_flg_i21c	twomass_xsc	TWOMASS	K confusion flag for 21mag/sq." iso. circ. mag.	smallint	2			meta.code
k_flg_i21e	twomass_xsc	TWOMASS	K confusion flag for 21mag/sq." iso. ell. mag.	smallint	2			meta.code
k_flg_j21fc	twomass_xsc	TWOMASS	K confusion flag for 21mag/sq." iso. fid. circ. mag.	smallint	2			meta.code
k_flg_j21fe	twomass_xsc	TWOMASS	K confusion flag for 21mag/sq." iso. fid. ell. mag.	smallint	2			meta.code
k_flg_k20fc	twomass_xsc	TWOMASS	K confusion flag for 20mag/sq." iso. fid. circ. mag.	smallint	2			meta.code
k_flg_k20fe	twomass_sixx2_xsc	TWOMASS	K confusion flag for 20mag/sq.″ iso. fid. ell. mag	smallint	2
k_flg_k20fe	twomass_xsc	TWOMASS	K confusion flag for 20mag/sq." iso. fid. ell. mag.	smallint	2			meta.code
k_m	twomass_psc	TWOMASS	Default Ks-band magnitude	real	4	mag		phot.flux
k_m	twomass_sixx2_psc	TWOMASS	K selected "default" magnitude	real	4	mag
k_m_10	twomass_xsc	TWOMASS	K 10 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_15	twomass_xsc	TWOMASS	K 15 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_20	twomass_xsc	TWOMASS	K 20 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_25	twomass_xsc	TWOMASS	K 25 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_2mass	allwise_sc	WISE	2MASS Ks-band magnitude of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC Ks-band magnitude entry is "null".	float	8	mag
k_m_2mass	wise_allskysc	WISE	2MASS Ks-band magnitude or magnitude upper limit of the associated 2MASS PSC source. This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC Ks-band magnitude entry is default.	real	4	mag	-0.9999995e9
k_m_2mass	wise_prelimsc	WISE	2MASS Ks-band magnitude or magnitude upper limit of the associated 2MASS PSC source This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC Ks-band magnitude entry is default	real	4	mag	-0.9999995e9
k_m_30	twomass_xsc	TWOMASS	K 30 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_40	twomass_xsc	TWOMASS	K 40 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_5	twomass_xsc	TWOMASS	K 5 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_50	twomass_xsc	TWOMASS	K 50 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_60	twomass_xsc	TWOMASS	K 60 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_7	twomass_sixx2_xsc	TWOMASS	K 7 arcsec radius circular aperture magnitude	real	4	mag
k_m_7	twomass_xsc	TWOMASS	K 7 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_70	twomass_xsc	TWOMASS	K 70 arcsec radius circular aperture magnitude.	real	4	mag		phot.flux
k_m_c	twomass_xsc	TWOMASS	K Kron circular aperture magnitude.	real	4	mag		phot.flux
k_m_e	twomass_xsc	TWOMASS	K Kron elliptical aperture magnitude.	real	4	mag		phot.flux
k_m_ext	twomass_sixx2_xsc	TWOMASS	K mag from fit extrapolation	real	4	mag
k_m_ext	twomass_xsc	TWOMASS	K mag from fit extrapolation.	real	4	mag		phot.flux
k_m_fc	twomass_xsc	TWOMASS	K fiducial Kron circular magnitude.	real	4	mag		phot.flux
k_m_fe	twomass_xsc	TWOMASS	K fiducial Kron ell. mag aperture magnitude.	real	4	mag		phot.flux
k_m_i20c	twomass_xsc	TWOMASS	K 20mag/sq." isophotal circular ap. magnitude.	real	4	mag		phot.flux
k_m_i20e	twomass_xsc	TWOMASS	K 20mag/sq." isophotal elliptical ap. magnitude.	real	4	mag		phot.flux
k_m_i21c	twomass_xsc	TWOMASS	K 21mag/sq." isophotal circular ap. magnitude.	real	4	mag		phot.flux
k_m_i21e	twomass_xsc	TWOMASS	K 21mag/sq." isophotal elliptical ap. magnitude.	real	4	mag		phot.flux
k_m_j21fc	twomass_xsc	TWOMASS	K 21mag/sq." isophotal fiducial circ. ap. mag.	real	4	mag		phot.flux
k_m_j21fe	twomass_xsc	TWOMASS	K 21mag/sq." isophotal fiducial ell. ap. magnitude.	real	4	mag		phot.flux
k_m_k20fc	twomass_xsc	TWOMASS	K 20mag/sq." isophotal fiducial circ. ap. mag.	real	4	mag		phot.flux
K_M_K20FE	twomass	SIXDF	K 20mag/sq." isophotal fiducial ell. ap. magnitude	real	4	mag
k_m_k20fe	twomass_sixx2_xsc	TWOMASS	K 20mag/sq.″ isophotal fiducial ell. ap. magnitude	real	4	mag
k_m_k20fe	twomass_xsc	TWOMASS	K 20mag/sq." isophotal fiducial ell. ap. magnitude.	real	4	mag		phot.flux
k_m_stdap	twomass_psc	TWOMASS	Ks-band "standard" aperture magnitude.	real	4	mag		phot.flux
k_m_sys	twomass_xsc	TWOMASS	K system photometry magnitude.	real	4	mag		phot.flux
k_mnsurfb_eff	twomass_xsc	TWOMASS	K mean surface brightness at the half-light radius.	real	4	mag		phot.mag.sb
k_msig	twomass_sixx2_psc	TWOMASS	K "default" mag uncertainty	real	4	mag
k_msig_10	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 10 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_15	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 15 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_20	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 20 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_25	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 25 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_2mass	allwise_sc	WISE	2MASS Ks-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC Ks-band uncertainty entry is "null".	float	8	mag
k_msig_2mass	wise_allskysc	WISE	2MASS Ks-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC Ks-band uncertainty entry is default.	real	4	mag	-0.9999995e9
k_msig_2mass	wise_prelimsc	WISE	2MASS Ks-band corrected photometric uncertainty of the associated 2MASS PSC source This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC Ks-band uncertainty entry is default	real	4	mag	-0.9999995e9
k_msig_30	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 30 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_40	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 40 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_5	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 5 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_50	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 50 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_60	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 60 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_7	twomass_sixx2_xsc	TWOMASS	K 1-sigma uncertainty in 7 arcsec circular ap. mag	real	4	mag
k_msig_7	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 7 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_70	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 70 arcsec circular ap. mag.	real	4	mag		stat.error
k_msig_c	twomass_xsc	TWOMASS	K 1-sigma uncertainty in Kron circular mag.	real	4	mag		stat.error
k_msig_e	twomass_xsc	TWOMASS	K 1-sigma uncertainty in Kron elliptical mag.	real	4	mag		stat.error
k_msig_ext	twomass_sixx2_xsc	TWOMASS	K 1-sigma uncertainty in mag from fit extrapolation	real	4	mag
k_msig_ext	twomass_xsc	TWOMASS	K 1-sigma uncertainty in mag from fit extrapolation.	real	4	mag		stat.error
k_msig_fc	twomass_xsc	TWOMASS	K 1-sigma uncertainty in fiducial Kron circ. mag.	real	4	mag		stat.error
k_msig_fe	twomass_xsc	TWOMASS	K 1-sigma uncertainty in fiducial Kron ell. mag.	real	4	mag		stat.error
k_msig_i20c	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 20mag/sq." iso. circ. mag.	real	4	mag		stat.error
k_msig_i20e	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 20mag/sq." iso. ell. mag.	real	4	mag		stat.error
k_msig_i21c	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 21mag/sq." iso. circ. mag.	real	4	mag		stat.error
k_msig_i21e	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 21mag/sq." iso. ell. mag.	real	4	mag		stat.error
k_msig_j21fc	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 21mag/sq." iso.fid.circ.mag.	real	4	mag		stat.error
k_msig_j21fe	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 21mag/sq." iso.fid.ell.mag.	real	4	mag		stat.error
k_msig_k20fc	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 20mag/sq." iso.fid.circ. mag.	real	4	mag		stat.error
k_msig_k20fe	twomass_sixx2_xsc	TWOMASS	K 1-sigma uncertainty in 20mag/sq.″ iso.fid.ell.mag	real	4	mag
k_msig_k20fe	twomass_xsc	TWOMASS	K 1-sigma uncertainty in 20mag/sq." iso.fid.ell.mag.	real	4	mag		stat.error
k_msig_stdap	twomass_psc	TWOMASS	Uncertainty in the Ks-band standard aperture magnitude.	real	4	mag		phot.flux
k_msig_sys	twomass_xsc	TWOMASS	K 1-sigma uncertainty in system photometry mag.	real	4	mag		stat.error
k_msigcom	twomass_psc	TWOMASS	Combined, or total photometric uncertainty for the default Ks-band magnitude.	real	4	mag	Ks-band	phot.flux
k_msigcom	twomass_sixx2_psc	TWOMASS	combined (total) K band photometric uncertainty	real	4	mag
k_msnr10	twomass_scn	TWOMASS	The estimated Ks-band magnitude at which SNR=10 is achieved for this scan.	real	4	mag		phot.flux
k_msnr10	twomass_sixx2_scn	TWOMASS	K mag at which SNR=10 is achieved, from k_psp and k_zp_ap	real	4	mag
k_n_snr10	twomass_scn	TWOMASS	Number of point sources at Ks-band with SNR>10 (instrumental mag <=14.3)	int	4			meta.number
k_n_snr10	twomass_sixx2_scn	TWOMASS	number of K point sources with SNR>10 (instrumental m<=14.3)	int	4
k_pchi	twomass_xsc	TWOMASS	K chi^2 of fit to rad. profile (LCSB: alpha scale len).	real	4			stat.fit.param
k_peak	twomass_xsc	TWOMASS	K peak pixel brightness.	real	4	mag		phot.mag.sb
k_perc_darea	twomass_xsc	TWOMASS	K 5-sigma to 3-sigma percent area change.	smallint	2			FIT_PARAM
k_phi	twomass_sixx2_xsc	TWOMASS	K angle to 3-sigma major axis (E of N)	smallint	2	deg
k_phi	twomass_xsc	TWOMASS	K angle to 3-sigma major axis (E of N).	smallint	2	degrees		pos.posAng
k_psfchi	twomass_psc	TWOMASS	Reduced chi-squared goodness-of-fit value for the Ks-band profile-fit photometry made on the 1.3 s "Read_2" exposures.	real	4			stat.fit.param
k_psp	twomass_scn	TWOMASS	Ks-band photometric sensitivity paramater (PSP).	real	4			instr.sensitivity
k_psp	twomass_sixx2_scn	TWOMASS	K photometric sensitivity param: k_shape_avg*(k_fbg_avg^.29)	real	4
k_pts_noise	twomass_scn	TWOMASS	Base-10 logarithm of the mode of the noise distribution for all point source detections in the scan, where the noise is estimated from the measured Ks-band photometric errors and is expressed in units of mJy.	real	4			instr.det.noise
k_pts_noise	twomass_sixx2_scn	TWOMASS	log10 of K band modal point src noise estimate	real	4	logmJy
k_r_c	twomass_xsc	TWOMASS	K Kron circular aperture radius.	real	4	arcsec		phys.angSize;src
k_r_e	twomass_xsc	TWOMASS	K Kron elliptical aperture semi-major axis.	real	4	arcsec		phys.angSize;src
k_r_eff	twomass_xsc	TWOMASS	K half-light (integrated half-flux point) radius.	real	4	arcsec		phys.angSize;src
k_r_i20c	twomass_xsc	TWOMASS	K 20mag/sq." isophotal circular aperture radius.	real	4	arcsec		phys.angSize;src
k_r_i20e	twomass_xsc	TWOMASS	K 20mag/sq." isophotal elliptical ap. semi-major axis.	real	4	arcsec		phys.angSize;src
k_r_i21c	twomass_xsc	TWOMASS	K 21mag/sq." isophotal circular aperture radius.	real	4	arcsec		phys.angSize;src
k_r_i21e	twomass_xsc	TWOMASS	K 21mag/sq." isophotal elliptical ap. semi-major axis.	real	4	arcsec		phys.angSize;src
k_resid_ann	twomass_xsc	TWOMASS	K residual annulus background median.	real	4	DN		meta.code
k_sc_1mm	twomass_xsc	TWOMASS	K 1st moment (score) (LCSB: super blk 2,4,8 SNR).	real	4			meta.code
k_sc_2mm	twomass_xsc	TWOMASS	K 2nd moment (score) (LCSB: SNRMAX - super SNR max).	real	4			meta.code
k_sc_msh	twomass_xsc	TWOMASS	K median shape score.	real	4			meta.code
k_sc_mxdn	twomass_xsc	TWOMASS	K mxdn (score) (LCSB: BSNR - block/smoothed SNR).	real	4			meta.code
k_sc_r1	twomass_xsc	TWOMASS	K r1 (score).	real	4			meta.code
k_sc_r23	twomass_xsc	TWOMASS	K r23 (score) (LCSB: TSNR - integrated SNR for r=15).	real	4			meta.code
k_sc_sh	twomass_xsc	TWOMASS	K shape (score).	real	4			meta.code
k_sc_vint	twomass_xsc	TWOMASS	K vint (score).	real	4			meta.code
k_sc_wsh	twomass_xsc	TWOMASS	K wsh (score) (LCSB: PSNR - peak raw SNR).	real	4			meta.code
k_seetrack	twomass_xsc	TWOMASS	K band seetracking score.	real	4			meta.code
k_sh0	twomass_xsc	TWOMASS	K ridge shape (LCSB: BSNR limit).	real	4			FIT_PARAM
k_shape_avg	twomass_scn	TWOMASS	Ks-band average seeing shape for scan.	real	4			instr.obsty.seeing
k_shape_avg	twomass_sixx2_scn	TWOMASS	K band average seeing shape for scan	real	4
k_shape_rms	twomass_scn	TWOMASS	RMS-error of Ks-band average seeing shape.	real	4			instr.obsty.seeing
k_shape_rms	twomass_sixx2_scn	TWOMASS	rms of K band avg seeing shape for scan	real	4
k_sig_sh0	twomass_xsc	TWOMASS	K ridge shape sigma (LCSB: B2SNR limit).	real	4			FIT_PARAM
k_snr	twomass_psc	TWOMASS	Ks-band "scan" signal-to-noise ratio.	real	4	mag		instr.det.noise
k_snr	twomass_sixx2_psc	TWOMASS	K band "scan" signal-to-noise ratio	real	4
k_subst2	twomass_xsc	TWOMASS	K residual background #2 (score).	real	4			meta.code
k_zp_ap	twomass_scn	TWOMASS	Photometric zero-point for Ks-band aperture photometry.	real	4	mag		phot.mag;arith.zp
k_zp_ap	twomass_sixx2_scn	TWOMASS	K band ap. calibration photometric zero-point for scan	real	4	mag
k_zperr_ap	twomass_scn	TWOMASS	RMS-error of zero-point for Ks-band aperture photometry	real	4	mag		stat.error
k_zperr_ap	twomass_sixx2_scn	TWOMASS	K band ap. calibration rms error of zero-point for scan	real	4	mag
ka	catwise_2020, catwise_prelim	WISE	astrometry usage code (0: neither the ascending nor the descending scan provided a solution; 1: only the ascending scan provided a solution; 2: only the descending scan provided a solution; 3: both scans provided solutions which were combined in the relevant way)	int	4
KBESTR	spectra	SIXDF	cross-correlation template	int	4
kCorr	twompzPhotoz	TWOMPZ	K 20mag/sq." isophotal fiducial ell. ap. magnitude with Galactic dust correction {image primary HDU keyword: Kcorr}	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
kCorrErr	twompzPhotoz	TWOMPZ	K 1-sigma uncertainty in 20mag/sq." aperture {image primary HDU keyword: k_msig_k20fe}	real	4	mag	-0.9999995e9
KEXT	twomass	SIXDF	KEXT magnitude	real	4	mag
KEXT_K	twomass	SIXDF	KEXT minus K (corrected)	real	4	mag
kFi2	vvvVivaCatalogue	VVVDR5	Even dispersion parameter of K_s pawprint data {catalogue TType keyword: Kfi2}	float	8	mag	-9.999995e8
km	catwise_2020, catwise_prelim	WISE	proper motion usage code (0: neither the ascending nor the descending scan provided a solution; 1: only the ascending scan provided a solution; 2: only the descending scan provided a solution; 3: both scans provided solutions which were combined in the relevant way)	int	4
Kmag	mcps_lmcSource, mcps_smcSource	MCPS	The K' band magnitude (from 2MASS) (0.00 if star not detected.)	real	4	mag
kMag	ukirtFSstars	VIDEOv20100513	K band total magnitude on the MKO(UFTI) system	real	4	mag		phot.mag
kMag	ukirtFSstars	VIKINGv20110714	K band total magnitude on the MKO(UFTI) system	real	4	mag		phot.mag
kMag	ukirtFSstars	VVVv20100531	K band total magnitude on the MKO(UFTI) system	real	4	mag		phot.mag
Kmag2MASS	spitzer_smcSource	SPITZER	The 2MASS K band magnitude.	real	4	mag
Kmag_2MASS	ravedr5Source	RAVE	K selected default magnitude from 2MASS	real	4	mag	magnitude	phot.mag;em.IR.K
Kmag_DENIS	ravedr5Source	RAVE	K selected default magnitude	real	4	mag	magnitude	phot.mag;em.IR.K
kMagErr	ukirtFSstars	VIDEOv20100513	K band magnitude error	real	4	mag		stat.error
kMagErr	ukirtFSstars	VIKINGv20110714	K band magnitude error	real	4	mag		stat.error
kMagErr	ukirtFSstars	VVVv20100531	K band magnitude error	real	4	mag		stat.error
kronFlux	Detection	PS1DR2	Kron (1980) flux.	real	4	Janskys	-999
kronFlux	sharksDetection	SHARKSv20210222	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	sharksDetection	SHARKSv20210421	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	ultravistaDetection	ULTRAVISTADR4	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	ultravistaMapRemeasurement	ULTRAVISTADR4	flux within Kron radius circular aperture (SE: FLUX_AUTO; CASU: default) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSDR2	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vhsDetection	VHSDR3	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSDR4	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSDR5	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSDR6	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20120926	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20130417	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20140409	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20150108	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20160114	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20160507	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20170630	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20180419	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20201209	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20231101	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection	VHSv20240731	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vhsDetection, vhsListRemeasurement	VHSDR1	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	videoDetection	VIDEODR2	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	videoDetection	VIDEODR3	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	videoDetection	VIDEODR4	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	videoDetection	VIDEODR5	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	videoDetection	VIDEOv20100513	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	videoDetection	VIDEOv20111208	flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	videoListRemeasurement	VIDEOv20100513	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vikingDetection	VIKINGDR2	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vikingDetection	VIKINGDR3	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGDR4	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20111019	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vikingDetection	VIKINGv20130417	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20140402	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20150421	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20151230	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20160406	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20161202	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection	VIKINGv20170715	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vikingDetection, vikingListRemeasurement	VIKINGv20110714	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vikingMapRemeasurement	VIKINGZYSELJv20160909	flux within Kron radius circular aperture (SE: FLUX_AUTO; CASU: default) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vikingMapRemeasurement	VIKINGZYSELJv20170124	flux within Kron radius circular aperture (SE: FLUX_AUTO; CASU: default) {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vmcDetection	VMCDR1	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vmcDetection	VMCDR2	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCDR3	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCDR4	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCDR5	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20110909	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vmcDetection	VMCv20120126	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vmcDetection	VMCv20121128	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20130304	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20130805	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20140428	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20140903	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20150309	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20151218	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20160311	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20160822	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20170109	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20170411	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20171101	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20180702	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20181120	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20191212	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20210708	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20230816	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection	VMCv20240226	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcDetection, vmcListRemeasurement	VMCv20110816	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFlux	vmcdeepDetection	VMCDEEPv20230713	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vmcdeepDetection	VMCDEEPv20240506	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vvvDetection	VVVDR1	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vvvDetection	VVVDR2	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count
kronFlux	vvvDetection, vvvListRemeasurement	VVVv20100531	flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux}	real	4	ADU		phot.count;em.opt
kronFluxErr	Detection	PS1DR2	Error on Kron (1980) flux.	real	4	Janskys	-999
kronFluxErr	sharksDetection	SHARKSv20210222	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	sharksDetection	SHARKSv20210421	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	ultravistaDetection	ULTRAVISTADR4	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	ultravistaMapRemeasurement	ULTRAVISTADR4	error on Kron flux (SE: FLUXERR_AUTO; CASU: default) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSDR2	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSDR3	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSDR4	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSDR5	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSDR6	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20120926	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20130417	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20140409	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20150108	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20160114	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20160507	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20170630	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20180419	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20201209	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20231101	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection	VHSv20240731	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vhsDetection, vhsListRemeasurement	VHSDR1	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoDetection	VIDEODR2	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoDetection	VIDEODR3	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoDetection	VIDEODR4	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoDetection	VIDEODR5	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoDetection	VIDEOv20100513	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoDetection	VIDEOv20111208	error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	videoListRemeasurement	VIDEOv20100513	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGDR2	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGDR3	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGDR4	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20111019	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20130417	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20140402	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20150421	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20151230	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20160406	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20161202	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection	VIKINGv20170715	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingDetection, vikingListRemeasurement	VIKINGv20110714	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingMapRemeasurement	VIKINGZYSELJv20160909	error on Kron flux (SE: FLUXERR_AUTO; CASU: default) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vikingMapRemeasurement	VIKINGZYSELJv20170124	error on Kron flux (SE: FLUXERR_AUTO; CASU: default) {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCDR1	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCDR2	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCDR3	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCDR4	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCDR5	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20110909	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20120126	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20121128	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20130304	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20130805	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20140428	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20140903	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20150309	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20151218	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20160311	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20160822	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20170109	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20170411	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20171101	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20180702	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20181120	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20191212	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20210708	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20230816	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection	VMCv20240226	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcDetection, vmcListRemeasurement	VMCv20110816	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcdeepDetection	VMCDEEPv20230713	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vmcdeepDetection	VMCDEEPv20240506	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vvvDetection	VVVDR1	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vvvDetection	VVVDR2	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronFluxErr	vvvDetection, vvvListRemeasurement	VVVv20100531	error on Kron flux {catalogue TType keyword: Kron_flux_err}	real	4	ADU		stat.error
kronJky	ultravistaMapRemeasurement	ULTRAVISTADR4	Calibrated Kron flux within aperture r_k (CASU: default)	real	4	jansky		phot.mag
kronJky	vikingMapRemeasurement	VIKINGZYSELJv20160909	Calibrated Kron flux within aperture r_k (CASU: default)	real	4	jansky		phot.mag
kronJky	vikingMapRemeasurement	VIKINGZYSELJv20170124	Calibrated Kron flux within aperture r_k (CASU: default)	real	4	jansky		phot.mag
kronJkyErr	ultravistaMapRemeasurement	ULTRAVISTADR4	error on calibrated Kron flux	real	4	jansky (CASU: default)		stat.error
kronJkyErr	vikingMapRemeasurement	VIKINGZYSELJv20160909	error on calibrated Kron flux	real	4	jansky (CASU: default)		stat.error
kronJkyErr	vikingMapRemeasurement	VIKINGZYSELJv20170124	error on calibrated Kron flux	real	4	jansky (CASU: default)		stat.error
kronLup	ultravistaMapRemeasurement	ULTRAVISTADR4	Calibrated Kron luptitude within aperture r_k (CASU: default)	real	4	lup		phot.mag
kronLup	vikingMapRemeasurement	VIKINGZYSELJv20160909	Calibrated Kron luptitude within aperture r_k (CASU: default)	real	4	lup		phot.mag
kronLup	vikingMapRemeasurement	VIKINGZYSELJv20170124	Calibrated Kron luptitude within aperture r_k (CASU: default)	real	4	lup		phot.mag
kronLupErr	ultravistaMapRemeasurement	ULTRAVISTADR4	error on calibrated Kron luptitude	real	4	lup (CASU: default)		stat.error
kronLupErr	vikingMapRemeasurement	VIKINGZYSELJv20160909	error on calibrated Kron luptitude	real	4	lup (CASU: default)		stat.error
kronLupErr	vikingMapRemeasurement	VIKINGZYSELJv20170124	error on calibrated Kron luptitude	real	4	lup (CASU: default)		stat.error
kronMag	sharksDetection	SHARKSv20210222	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	sharksDetection	SHARKSv20210421	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	ultravistaDetection	ULTRAVISTADR4	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	ultravistaMapRemeasurement	ULTRAVISTADR4	Calibrated Kron magnitude within aperture r_k (CASU: default)	real	4	mag		phot.mag
kronMag	vhsDetection	VHSDR2	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSDR3	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSDR4	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSDR5	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSDR6	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20120926	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20130417	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20140409	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20150108	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20160114	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20160507	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20170630	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20180419	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20201209	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20231101	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection	VHSv20240731	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vhsDetection, vhsListRemeasurement	VHSDR1	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	videoDetection	VIDEODR2	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	videoDetection	VIDEODR3	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	videoDetection	VIDEODR4	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	videoDetection	VIDEODR5	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	videoDetection	VIDEOv20111208	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	videoDetection, videoListRemeasurement	VIDEOv20100513	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGDR2	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGDR3	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGDR4	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20111019	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20130417	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20140402	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20150421	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20151230	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20160406	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20161202	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection	VIKINGv20170715	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingDetection, vikingListRemeasurement	VIKINGv20110714	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vikingMapRemeasurement	VIKINGZYSELJv20160909	Calibrated Kron magnitude within aperture r_k (CASU: default)	real	4	mag		phot.mag
kronMag	vikingMapRemeasurement	VIKINGZYSELJv20170124	Calibrated Kron magnitude within aperture r_k (CASU: default)	real	4	mag		phot.mag
kronMag	vmcDetection	VMCDR1	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCDR2	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCDR3	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCDR4	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCDR5	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20110909	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20120126	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20121128	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20130304	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20130805	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20140428	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20140903	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20150309	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20151218	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20160311	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20160822	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20170109	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20170411	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20171101	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20180702	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20181120	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20191212	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20210708	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20230816	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection	VMCv20240226	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcDetection, vmcListRemeasurement	VMCv20110816	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcdeepDetection	VMCDEEPv20230713	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vmcdeepDetection	VMCDEEPv20240506	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vvvDetection	VVVDR1	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vvvDetection	VVVDR2	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMag	vvvDetection, vvvListRemeasurement	VVVv20100531	Calibrated Kron magnitude within circular aperture r_k	real	4	mag		phot.mag
kronMagErr	sharksDetection	SHARKSv20210222	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	sharksDetection	SHARKSv20210421	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	ultravistaDetection	ULTRAVISTADR4	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	ultravistaMapRemeasurement	ULTRAVISTADR4	error on calibrated Kron magnitude	real	4	mag (CASU: default)		stat.error
kronMagErr	vhsDetection	VHSDR2	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vhsDetection	VHSDR3	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vhsDetection	VHSDR4	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSDR5	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSDR6	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20120926	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vhsDetection	VHSv20130417	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vhsDetection	VHSv20140409	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vhsDetection	VHSv20150108	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20160114	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20160507	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20170630	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20180419	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20201209	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20231101	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection	VHSv20240731	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vhsDetection, vhsListRemeasurement	VHSDR1	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	videoDetection	VIDEODR2	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	videoDetection	VIDEODR3	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	videoDetection	VIDEODR4	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	videoDetection	VIDEODR5	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	videoDetection	VIDEOv20111208	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	videoDetection, videoListRemeasurement	VIDEOv20100513	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGDR2	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGDR3	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGDR4	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGv20111019	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGv20130417	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGv20140402	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingDetection	VIKINGv20150421	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vikingDetection	VIKINGv20151230	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vikingDetection	VIKINGv20160406	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vikingDetection	VIKINGv20161202	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vikingDetection	VIKINGv20170715	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vikingDetection, vikingListRemeasurement	VIKINGv20110714	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vikingMapRemeasurement	VIKINGZYSELJv20160909	error on calibrated Kron magnitude	real	4	mag (CASU: default)		stat.error
kronMagErr	vikingMapRemeasurement	VIKINGZYSELJv20170124	error on calibrated Kron magnitude	real	4	mag (CASU: default)		stat.error
kronMagErr	vmcDetection	VMCDR1	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCDR2	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCDR3	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCDR4	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCDR5	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20110909	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCv20120126	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCv20121128	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCv20130304	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCv20130805	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCv20140428	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcDetection	VMCv20140903	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20150309	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20151218	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20160311	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20160822	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20170109	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20170411	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20171101	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20180702	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20181120	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20191212	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20210708	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20230816	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection	VMCv20240226	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcDetection, vmcListRemeasurement	VMCv20110816	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vmcdeepDetection	VMCDEEPv20230713	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vmcdeepDetection	VMCDEEPv20240506	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vvvDetection	VVVDR1	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vvvDetection	VVVDR2	error on calibrated Kron magnitude	real	4	mag		stat.error
kronMagErr	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	error on calibrated Kron magnitude	real	4	mag		stat.error;phot.mag
kronMagErr	vvvDetection, vvvListRemeasurement	VVVv20100531	error on calibrated Kron magnitude	real	4	mag		stat.error
kronRad	Detection	PS1DR2	Kron (1980) radius.	real	4	arcsec	-999
kronRad	sharksDetection	SHARKSv20210222	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	sharksDetection	SHARKSv20210421	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	ultravistaDetection	ULTRAVISTADR4	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	ultravistaMapRemeasurement	ULTRAVISTADR4	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	vhsDetection	VHSDR2	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vhsDetection	VHSDR3	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSDR4	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSDR5	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSDR6	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20120926	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20130417	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20140409	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20150108	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20160114	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20160507	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20170630	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20180419	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20201209	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20231101	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection	VHSv20240731	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vhsDetection, vhsListRemeasurement	VHSDR1	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	videoDetection	VIDEODR2	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize;src
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	videoDetection	VIDEODR3	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	videoDetection	VIDEODR4	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	videoDetection	VIDEODR5	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	videoDetection	VIDEOv20100513	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize;src
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	videoDetection	VIDEOv20111208	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize;src
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	videoListRemeasurement	VIDEOv20100513	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vikingDetection	VIKINGDR2	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vikingDetection	VIKINGDR3	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGDR4	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20111019	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vikingDetection	VIKINGv20130417	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20140402	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20150421	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20151230	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20160406	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20161202	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection	VIKINGv20170715	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vikingDetection, vikingListRemeasurement	VIKINGv20110714	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vikingMapRemeasurement	VIKINGZYSELJv20160909	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize;src
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	vikingMapRemeasurement	VIKINGZYSELJv20170124	Kron radius as defined in SE by Graham and Driver (2005) (SE: KRON_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Kron_radius} r_k = ∑R² I(R) / ∑R I(R)	real	4	pixels		phys.angSize;src
			Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
kronRad	vmcDetection	VMCDR1	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vmcDetection	VMCDR2	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCDR3	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCDR4	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCDR5	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20110909	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vmcDetection	VMCv20120126	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vmcDetection	VMCv20121128	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20130304	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20130805	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20140428	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20140903	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20150309	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20151218	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20160311	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20160822	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20170109	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20170411	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20171101	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20180702	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20181120	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20191212	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20210708	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20230816	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection	VMCv20240226	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcDetection, vmcListRemeasurement	VMCv20110816	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
kronRad	vmcdeepDetection	VMCDEEPv20230713	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vmcdeepDetection	VMCDEEPv20240506	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vvvDetection	VVVDR1	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vvvDetection	VVVDR2	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize
kronRad	vvvDetection, vvvListRemeasurement	VVVv20100531	r_k as defined in Bertin and Arnouts 1996 A&A Supp 117 393 {catalogue TType keyword: Kron_radius}	real	4	pixels		phys.angSize;src
ks_1AperMag1	vvvSource	VVVDR5	Point source Ks_1 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_1AperMag1	vvvxSource	VVVXDR1	Point source Ks_1 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_1AperMag1Err	vvvSource	VVVDR5	Error in point source Ks_1 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_1AperMag1Err	vvvxSource	VVVXDR1	Error in point source Ks_1 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_1AperMag3	vvvSource	VVVDR5	Default point source Ks_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_1AperMag3	vvvxSource	VVVXDR1	Default point source Ks_1 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_1AperMag3Err	vvvSource	VVVDR5	Error in default point source Ks_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_1AperMag3Err	vvvxSource	VVVXDR1	Error in default point source Ks_1 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_1AperMag4	vvvSource	VVVDR5	Point source Ks_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_1AperMag4	vvvxSource	VVVXDR1	Point source Ks_1 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_1AperMag4Err	vvvSource	VVVDR5	Error in point source Ks_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_1AperMag4Err	vvvxSource	VVVXDR1	Error in point source Ks_1 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_1AverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) Ks_1	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ks_1AverageConf	vvvxSource	VVVXDR1	average confidence in 2 arcsec diameter default aperture (aper3) Ks_1	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ks_1Class	vvvSource	VVVDR5	discrete image classification flag in Ks_1	smallint	2		-9999	src.class;em.IR.K
ks_1Class	vvvxSource	VVVXDR1	discrete image classification flag in Ks_1	smallint	2		-9999	src.class;em.IR.K
ks_1ClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in Ks_1	real	4		-0.9999995e9	stat;em.IR.K
ks_1ClassStat	vvvxSource	VVVXDR1	S-Extractor classification statistic in Ks_1	real	4		-0.9999995e9	stat;em.IR.K
ks_1Ell	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in Ks_1	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ks_1Ell	vvvxSource	VVVXDR1	1-b/a, where a/b=semi-major/minor axes in Ks_1	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ks_1eNum	vvvMergeLog	VVVDR5	the extension number of this Ks_1 frame	tinyint	1			meta.number;em.IR.K
ks_1eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the extension number of this 1st epoch Ks frame	tinyint	1			meta.number;em.IR.K
ks_1eNum	vvvxMergeLog	VVVXDR1	the extension number of this Ks_1 frame	tinyint	1			meta.id;em.IR.K
ks_1ErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in Ks_1	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ks_1ErrBits	vvvxSource	VVVXDR1	processing warning/error bitwise flags in Ks_1	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ks_1Eta	vvvSource	VVVDR5	Offset of Ks_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_1Eta	vvvxSource	VVVXDR1	Offset of Ks_1 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_1Gausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in Ks_1	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ks_1Gausig	vvvxSource	VVVXDR1	RMS of axes of ellipse fit in Ks_1	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ks_1mfID	vvvMergeLog	VVVDR5	the UID of the relevant Ks_1 multiframe	bigint	8			meta.id;obs.field;em.IR.K
ks_1mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the UID of the relevant 1st epoch Ks tile multiframe	bigint	8			meta.id;obs.field;em.IR.K
ks_1mfID	vvvxMergeLog	VVVXDR1	the UID of the relevant Ks_1 multiframe	bigint	8			meta.id;obs.field;em.IR.K
ks_1Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the MJD of the 1st epoch Ks tile multiframe	float	8			time;em.IR.K
ks_1Mjd	vvvSource	VVVDR5	Modified Julian Day in Ks_1 band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ks_1Mjd	vvvxSource	VVVXDR1	Modified Julian Day in Ks_1 band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ks_1PA	vvvSource	VVVDR5	ellipse fit celestial orientation in Ks_1	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ks_1PA	vvvxSource	VVVXDR1	ellipse fit celestial orientation in Ks_1	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ks_1ppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in Ks_1	int	4		0	meta.code;em.IR.K
ks_1ppErrBits	vvvxSource	VVVXDR1	additional WFAU post-processing error bits in Ks_1	int	4		0	meta.code;em.IR.K
ks_1SeqNum	vvvSource	VVVDR5	the running number of the Ks_1 detection	int	4		-99999999	meta.number;em.IR.K
ks_1SeqNum	vvvxSource	VVVXDR1	the running number of the Ks_1 detection	int	4		-99999999	meta.id;em.IR.K
ks_1Xi	vvvSource	VVVDR5	Offset of Ks_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_1Xi	vvvxSource	VVVXDR1	Offset of Ks_1 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_2AperMag1	vvvSource	VVVDR5	Point source Ks_2 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_2AperMag1	vvvxSource	VVVXDR1	Point source Ks_2 aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_2AperMag1Err	vvvSource	VVVDR5	Error in point source Ks_2 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_2AperMag1Err	vvvxSource	VVVXDR1	Error in point source Ks_2 mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_2AperMag3	vvvSource	VVVDR5	Default point source Ks_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_2AperMag3	vvvxSource	VVVXDR1	Default point source Ks_2 aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_2AperMag3Err	vvvSource	VVVDR5	Error in default point source Ks_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_2AperMag3Err	vvvxSource	VVVXDR1	Error in default point source Ks_2 mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_2AperMag4	vvvSource	VVVDR5	Point source Ks_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_2AperMag4	vvvxSource	VVVXDR1	Point source Ks_2 aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ks_2AperMag4Err	vvvSource	VVVDR5	Error in point source Ks_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_2AperMag4Err	vvvxSource	VVVXDR1	Error in point source Ks_2 mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ks_2AverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) Ks_2	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ks_2AverageConf	vvvxSource	VVVXDR1	average confidence in 2 arcsec diameter default aperture (aper3) Ks_2	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ks_2Class	vvvSource	VVVDR5	discrete image classification flag in Ks_2	smallint	2		-9999	src.class;em.IR.K
ks_2Class	vvvxSource	VVVXDR1	discrete image classification flag in Ks_2	smallint	2		-9999	src.class;em.IR.K
ks_2ClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in Ks_2	real	4		-0.9999995e9	stat;em.IR.K
ks_2ClassStat	vvvxSource	VVVXDR1	S-Extractor classification statistic in Ks_2	real	4		-0.9999995e9	stat;em.IR.K
ks_2Ell	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in Ks_2	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ks_2Ell	vvvxSource	VVVXDR1	1-b/a, where a/b=semi-major/minor axes in Ks_2	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ks_2eNum	vvvMergeLog	VVVDR5	the extension number of this Ks_2 frame	tinyint	1			meta.number;em.IR.K
ks_2eNum	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the extension number of this 2nd epoch Ks frame	tinyint	1			meta.number;em.IR.K
ks_2eNum	vvvxMergeLog	VVVXDR1	the extension number of this Ks_2 frame	tinyint	1			meta.id;em.IR.K
ks_2ErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in Ks_2	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ks_2ErrBits	vvvxSource	VVVXDR1	processing warning/error bitwise flags in Ks_2	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ks_2Eta	vvvSource	VVVDR5	Offset of Ks_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_2Eta	vvvxSource	VVVXDR1	Offset of Ks_2 detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_2Gausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in Ks_2	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ks_2Gausig	vvvxSource	VVVXDR1	RMS of axes of ellipse fit in Ks_2	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ks_2mfID	vvvMergeLog	VVVDR5	the UID of the relevant Ks_2 multiframe	bigint	8			meta.id;obs.field;em.IR.K
ks_2mfID	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the UID of the relevant 2nd epoch Ks tile multiframe	bigint	8			meta.id;obs.field;em.IR.K
ks_2mfID	vvvxMergeLog	VVVXDR1	the UID of the relevant Ks_2 multiframe	bigint	8			meta.id;obs.field;em.IR.K
ks_2Mjd	vvvPsfDophotZYJHKsMergeLog	VVVDR5	the MJD of the 2nd epoch Ks tile multiframe	float	8			time;em.IR.K
ks_2Mjd	vvvSource	VVVDR5	Modified Julian Day in Ks_2 band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ks_2Mjd	vvvxSource	VVVXDR1	Modified Julian Day in Ks_2 band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ks_2PA	vvvSource	VVVDR5	ellipse fit celestial orientation in Ks_2	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ks_2PA	vvvxSource	VVVXDR1	ellipse fit celestial orientation in Ks_2	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ks_2ppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in Ks_2	int	4		0	meta.code;em.IR.K
ks_2ppErrBits	vvvxSource	VVVXDR1	additional WFAU post-processing error bits in Ks_2	int	4		0	meta.code;em.IR.K
ks_2SeqNum	vvvSource	VVVDR5	the running number of the Ks_2 detection	int	4		-99999999	meta.number;em.IR.K
ks_2SeqNum	vvvxSource	VVVXDR1	the running number of the Ks_2 detection	int	4		-99999999	meta.id;em.IR.K
ks_2Xi	vvvSource	VVVDR5	Offset of Ks_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ks_2Xi	vvvxSource	VVVXDR1	Offset of Ks_2 detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksAmpl	vmcCepheidVariables	VMCDR3	Amplitude of Ks band magnitude variation {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCDR4	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20121128	Amplitude of Ks band magnitude variation {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.NIR
ksAmpl	vmcCepheidVariables	VMCv20140428	Amplitude of Ks band magnitude variation {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20140903	Amplitude of Ks band magnitude variation {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20150309	Amplitude of Ks band magnitude variation {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20151218	Amplitude of Ks band magnitude variation {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20160311	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20160822	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20170109	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20170411	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20171101	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20180702	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20181120	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20191212	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20210708	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20230816	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcCepheidVariables	VMCv20240226	Peak-to-Peak amplitude in Ks band {catalogue TType keyword: A(Ks)}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCDR4	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20160822	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20170109	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20170411	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20171101	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20180702	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20181120	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20191212	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20210708	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmpl	vmcRRlyraeVariables	VMCv20230816	Amplitude in K_s band provided by GRATIS {catalogue TType keyword: AMPL_K}	real	4	mag	-0.9999995e9	src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCDR4	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20160311	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20160822	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20170109	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20170411	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20171101	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20180702	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20181120	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20191212	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20210708	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20230816	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAmplErr	vmcCepheidVariables	VMCv20240226	Error in Peak-to-Peak amplitude in Ks band {catalogue TType keyword: e_A(Ks)}	real	4	mag	-0.9999995e9	stat.error;src.var.amplitude;em.IR.K
ksAperJky3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source Ks (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source Ks (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source Ks (2.0 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Ks aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Ks aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Ks aperture corrected (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Ks (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Ks (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Ks (2.8 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Ks aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Ks aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Ks aperture corrected (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJky6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Ks (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Ks (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJky6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Ks (5.7 arcsec aperture diameter) calibrated flux	real	4	jansky	-0.9999995e9	stat.error
ksAperJkyNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux If in doubt use this flux estimator	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperJkyNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux	real	4	jansky	-0.9999995e9	phot.flux
ksAperLup3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
ksAperLup3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
ksAperLup3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
ksAperLup3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source Ks (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source Ks (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source Ks (2.0 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Ks aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLup4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Ks aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLup4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Ks aperture corrected (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLup4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Ks (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Ks (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Ks (2.8 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Ks aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLup6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Ks aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLup6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Ks aperture corrected (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLup6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Ks (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Ks (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLup6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Ks (5.7 arcsec aperture diameter) luptitude	real	4	lup	-0.9999995e9	stat.error
ksAperLupNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude If in doubt use this flux estimator	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperLupNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude	real	4	lup	-0.9999995e9	phot.lup
ksAperMag1	vmcSynopticSource	VMCDR1	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vmcSynopticSource	VMCDR2	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCDR3	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCDR4	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCDR5	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20110816	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vmcSynopticSource	VMCv20110909	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vmcSynopticSource	VMCv20120126	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vmcSynopticSource	VMCv20121128	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vmcSynopticSource	VMCv20130304	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vmcSynopticSource	VMCv20130805	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20140428	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20140903	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20150309	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20151218	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20160311	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20160822	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20170109	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20170411	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20171101	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20180702	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20181120	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20191212	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20210708	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20230816	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcSynopticSource	VMCv20240226	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vvvSource	VVVDR1	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vvvSource	VVVDR5	Point source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vvvSource	VVVv20100531	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vvvSource	VVVv20110718	Extended source Ks aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag1	vvvSource, vvvSynopticSource	VVVDR2	Extended source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1	vvvxSource	VVVXDR1	Point source Ks aperture corrected mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCDR1	Error in extended source Ks mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCDR2	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCDR3	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag1Err	vmcSynopticSource	VMCDR4	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCDR5	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20110816	Error in extended source Ks mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCv20110909	Error in extended source Ks mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCv20120126	Error in extended source Ks mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCv20121128	Error in extended source Ks mag (0.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCv20130304	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCv20130805	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vmcSynopticSource	VMCv20140428	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20140903	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag1Err	vmcSynopticSource	VMCv20150309	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag1Err	vmcSynopticSource	VMCv20151218	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20160311	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20160822	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20170109	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20170411	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20171101	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20180702	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20181120	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20191212	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20210708	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20230816	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcSynopticSource	VMCv20240226	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vvvSource	VVVDR1	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vvvSource	VVVDR5	Error in point source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag1Err	vvvSource	VVVv20100531	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vvvSource	VVVv20110718	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vvvSource, vvvSynopticSource	VVVDR2	Error in extended source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag1Err	vvvxSource	VVVXDR1	Error in point source Ks mag (1.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCDR1	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vmcSynopticSource	VMCDR2	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCDR3	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCDR4	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCDR5	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20110816	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vmcSynopticSource	VMCv20110909	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vmcSynopticSource	VMCv20120126	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vmcSynopticSource	VMCv20121128	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vmcSynopticSource	VMCv20130304	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vmcSynopticSource	VMCv20130805	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20140428	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20140903	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20150309	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20151218	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20160311	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20160822	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20170109	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20170411	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20171101	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20180702	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20181120	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20191212	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20210708	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20230816	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcSynopticSource	VMCv20240226	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2	vvvSynopticSource	VVVDR1	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag2	vvvSynopticSource	VVVDR2	Extended source Ks aperture corrected mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCDR1	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCDR2	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCDR3	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag2Err	vmcSynopticSource	VMCDR4	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCDR5	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20110816	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCv20110909	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCv20120126	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCv20121128	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCv20130304	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCv20130805	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vmcSynopticSource	VMCv20140428	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20140903	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag2Err	vmcSynopticSource	VMCv20150309	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag2Err	vmcSynopticSource	VMCv20151218	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20160311	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20160822	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20170109	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20170411	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20171101	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20180702	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20181120	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20191212	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20210708	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20230816	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcSynopticSource	VMCv20240226	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag2Err	vvvSynopticSource	VVVDR1	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag2Err	vvvSynopticSource	VVVDR2	Error in extended source Ks mag (1.4 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3	sharksSource	SHARKSv20210222	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	sharksSource	SHARKSv20210421	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	ultravistaSource	ULTRAVISTADR4	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vhsSource	VHSDR1	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vhsSource	VHSDR2	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vhsSource	VHSDR3	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSDR4	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSDR5	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSDR6	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20120926	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vhsSource	VHSv20130417	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vhsSource	VHSv20140409	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20150108	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20160114	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20160507	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20170630	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20180419	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20201209	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20231101	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vhsSource	VHSv20240731	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	videoSource	VIDEODR2	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	videoSource	VIDEODR3	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	videoSource	VIDEODR4	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	videoSource	VIDEODR5	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	videoSource	VIDEOv20100513	Default point/extended source Ks mag, no aperture correction applied If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	videoSource	VIDEOv20111208	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingSource	VIKINGDR2	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingSource	VIKINGDR3	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingSource	VIKINGDR4	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingSource	VIKINGv20110714	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingSource	VIKINGv20111019	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingSource	VIKINGv20130417	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingSource	VIKINGv20140402	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingSource	VIKINGv20150421	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingSource	VIKINGv20151230	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingSource	VIKINGv20160406	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingSource	VIKINGv20161202	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingSource	VIKINGv20170715	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default point source Ks aperture corrected (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCDR1	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCDR2	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCDR3	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCDR4	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCDR5	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20110816	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCv20110909	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCv20120126	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCv20121128	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCv20130304	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSource	VMCv20130805	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20140428	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20140903	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20150309	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20151218	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20160311	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20160822	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20170109	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20170411	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20171101	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20180702	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20181120	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20191212	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20210708	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20230816	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSource	VMCv20240226	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCDR1	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSynopticSource	VMCDR2	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCDR3	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCDR4	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCDR5	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20110816	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSynopticSource	VMCv20110909	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSynopticSource	VMCv20120126	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSynopticSource	VMCv20121128	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSynopticSource	VMCv20130304	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vmcSynopticSource	VMCv20130805	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20140428	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20140903	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20150309	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20151218	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20160311	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20160822	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20170109	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20170411	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20171101	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20180702	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20181120	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20191212	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20210708	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20230816	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcSynopticSource	VMCv20240226	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcdeepSource	VMCDEEPv20230713	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcdeepSource	VMCDEEPv20240506	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcdeepSynopticSource	VMCDEEPv20230713	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vmcdeepSynopticSource	VMCDEEPv20240506	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vvvSource	VVVDR1	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vvvSource	VVVDR2	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vvvSource	VVVDR5	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vvvSource	VVVv20100531	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vvvSource	VVVv20110718	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vvvSynopticSource	VVVDR1	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag3	vvvSynopticSource	VVVDR2	Default point/extended source Ks aperture corrected mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3	vvvVivaCatalogue	VVVDR5	ks magnitude using aperture corrected mag (2.0 arcsec aperture diameter, from VVVDR4 1st epoch JHKs contemporaneous OB) {catalogue TType keyword: ksAperMag3}	real	4	mag	-9.999995e8
ksAperMag3	vvvxSource	VVVXDR1	Default point source Ks aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag3Err	sharksSource	SHARKSv20210222	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	sharksSource	SHARKSv20210421	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	ultravistaSource	ULTRAVISTADR4	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in default point/extended source Ks (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vhsSource	VHSDR1	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vhsSource	VHSDR2	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vhsSource	VHSDR3	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag3Err	vhsSource	VHSDR4	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	vhsSource	VHSDR5	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSDR6	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20120926	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vhsSource	VHSv20130417	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vhsSource	VHSv20140409	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag3Err	vhsSource	VHSv20150108	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	vhsSource	VHSv20160114	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20160507	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20170630	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20180419	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20201209	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20231101	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vhsSource	VHSv20240731	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	videoSource	VIDEODR2	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	videoSource	VIDEODR3	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	videoSource	VIDEODR4	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	videoSource	VIDEODR5	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	videoSource	VIDEOv20100513	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	videoSource	VIDEOv20111208	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGDR2	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGDR3	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGDR4	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag3Err	vikingSource	VIKINGv20110714	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGv20111019	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGv20130417	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGv20140402	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingSource	VIKINGv20150421	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	vikingSource	VIKINGv20151230	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vikingSource	VIKINGv20160406	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vikingSource	VIKINGv20161202	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vikingSource	VIKINGv20170715	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in default point/extended source Ks (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in default point/extended source Ks (2.0 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCDR2	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCDR3	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	vmcSource	VMCDR4	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCDR5	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20110816	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCv20110909	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCv20120126	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCv20121128	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCv20130304	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCv20130805	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcSource	VMCv20140428	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag3Err	vmcSource	VMCv20140903	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	vmcSource	VMCv20150309	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag3Err	vmcSource	VMCv20151218	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20160311	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20160822	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20170109	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20170411	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20171101	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20180702	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20181120	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20191212	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20210708	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20230816	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource	VMCv20240226	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcSource, vmcSynopticSource	VMCDR1	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vmcdeepSource	VMCDEEPv20240506	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vvvSource	VVVDR2	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vvvSource	VVVDR5	Error in default point source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag3Err	vvvSource	VVVv20100531	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vvvSource	VVVv20110718	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vvvSource, vvvSynopticSource	VVVDR1	Error in default point/extended source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag3Err	vvvVivaCatalogue	VVVDR5	Error in default point source Ks mag, from VVVDR4 {catalogue TType keyword: ksAperMag3Err}	real	4	mag	-9.999995e8
ksAperMag3Err	vvvxSource	VVVXDR1	Error in default point source Ks mag (2.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4	sharksSource	SHARKSv20210222	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	sharksSource	SHARKSv20210421	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	ultravistaSource	ULTRAVISTADR4	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Ks aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vhsSource	VHSDR1	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vhsSource	VHSDR2	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vhsSource	VHSDR3	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSDR4	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSDR5	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSDR6	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20120926	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vhsSource	VHSv20130417	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vhsSource	VHSv20140409	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20150108	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20160114	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20160507	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20170630	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20180419	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20201209	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20231101	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vhsSource	VHSv20240731	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	videoSource	VIDEODR2	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	videoSource	VIDEODR3	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	videoSource	VIDEODR4	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	videoSource	VIDEODR5	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	videoSource	VIDEOv20100513	Extended source Ks mag, no aperture correction applied	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	videoSource	VIDEOv20111208	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingSource	VIKINGDR2	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingSource	VIKINGDR3	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingSource	VIKINGDR4	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingSource	VIKINGv20110714	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingSource	VIKINGv20111019	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingSource	VIKINGv20130417	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingSource	VIKINGv20140402	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingSource	VIKINGv20150421	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingSource	VIKINGv20151230	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingSource	VIKINGv20160406	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingSource	VIKINGv20161202	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingSource	VIKINGv20170715	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Ks aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Ks aperture corrected (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCDR1	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCDR2	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCDR3	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCDR4	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCDR5	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20110816	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCv20110909	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCv20120126	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCv20121128	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCv20130304	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSource	VMCv20130805	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20140428	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20140903	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20150309	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20151218	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20160311	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20160822	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20170109	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20170411	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20171101	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20180702	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20181120	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20191212	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20210708	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20230816	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSource	VMCv20240226	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCDR1	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSynopticSource	VMCDR2	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCDR3	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCDR4	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCDR5	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20110816	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSynopticSource	VMCv20110909	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSynopticSource	VMCv20120126	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSynopticSource	VMCv20121128	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSynopticSource	VMCv20130304	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vmcSynopticSource	VMCv20130805	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20140428	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20140903	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20150309	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20151218	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20160311	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20160822	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20170109	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20170411	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20171101	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20180702	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20181120	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20191212	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20210708	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20230816	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcSynopticSource	VMCv20240226	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcdeepSource	VMCDEEPv20230713	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcdeepSource	VMCDEEPv20240506	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vvvSource	VVVDR2	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vvvSource	VVVDR5	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4	vvvSource	VVVv20100531	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vvvSource	VVVv20110718	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vvvSource, vvvSynopticSource	VVVDR1	Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag4	vvvxSource	VVVXDR1	Point source Ks aperture corrected mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag4Err	sharksSource	SHARKSv20210222	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	sharksSource	SHARKSv20210421	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	ultravistaSource	ULTRAVISTADR4	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Ks (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vhsSource	VHSDR1	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vhsSource	VHSDR2	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vhsSource	VHSDR3	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag4Err	vhsSource	VHSDR4	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vhsSource	VHSDR5	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSDR6	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20120926	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vhsSource	VHSv20130417	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vhsSource	VHSv20140409	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag4Err	vhsSource	VHSv20150108	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vhsSource	VHSv20160114	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20160507	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20170630	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20180419	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20201209	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20231101	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vhsSource	VHSv20240731	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	videoSource	VIDEODR2	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	videoSource	VIDEODR3	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	videoSource	VIDEODR4	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	videoSource	VIDEODR5	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	videoSource	VIDEOv20100513	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	videoSource	VIDEOv20111208	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGDR2	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGDR3	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGDR4	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag4Err	vikingSource	VIKINGv20110714	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGv20111019	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGv20130417	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGv20140402	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingSource	VIKINGv20150421	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vikingSource	VIKINGv20151230	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vikingSource	VIKINGv20160406	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vikingSource	VIKINGv20161202	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vikingSource	VIKINGv20170715	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Ks (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Ks (2.8 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCDR1	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCDR2	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCDR3	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vmcSource	VMCDR4	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCDR5	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20110816	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCv20110909	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCv20120126	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCv20121128	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCv20130304	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCv20130805	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSource	VMCv20140428	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag4Err	vmcSource	VMCv20140903	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vmcSource	VMCv20150309	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vmcSource	VMCv20151218	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20160311	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20160822	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20170109	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20170411	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20171101	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20180702	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20181120	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20191212	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20210708	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20230816	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSource	VMCv20240226	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCDR1	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCDR2	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCDR3	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vmcSynopticSource	VMCDR4	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCDR5	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20110816	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCv20110909	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCv20120126	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCv20121128	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCv20130304	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCv20130805	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vmcSynopticSource	VMCv20140428	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20140903	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vmcSynopticSource	VMCv20150309	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag4Err	vmcSynopticSource	VMCv20151218	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20160311	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20160822	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20170109	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20170411	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20171101	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20180702	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20181120	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20191212	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20210708	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20230816	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcSynopticSource	VMCv20240226	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcdeepSource	VMCDEEPv20230713	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcdeepSource	VMCDEEPv20240506	Error in point/extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vvvSource	VVVDR2	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vvvSource	VVVDR5	Error in point source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag4Err	vvvSource	VVVv20100531	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vvvSource	VVVv20110718	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vvvSource, vvvSynopticSource	VVVDR1	Error in extended source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag4Err	vvvxSource	VVVXDR1	Error in point source Ks mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCDR1	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vmcSynopticSource	VMCDR2	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCDR3	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCDR4	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCDR5	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20110816	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vmcSynopticSource	VMCv20110909	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vmcSynopticSource	VMCv20120126	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vmcSynopticSource	VMCv20121128	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vmcSynopticSource	VMCv20130304	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vmcSynopticSource	VMCv20130805	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20140428	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20140903	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20150309	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20151218	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20160311	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20160822	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20170109	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20170411	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20171101	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20180702	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20181120	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20191212	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20210708	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20230816	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcSynopticSource	VMCv20240226	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcdeepSynopticSource	VMCDEEPv20230713	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vmcdeepSynopticSource	VMCDEEPv20240506	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5	vvvSynopticSource	VVVDR1	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag5	vvvSynopticSource	VVVDR2	Extended source Ks aperture corrected mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCDR1	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCDR2	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCDR3	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag5Err	vmcSynopticSource	VMCDR4	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCDR5	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20110816	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCv20110909	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCv20120126	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCv20121128	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCv20130304	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCv20130805	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vmcSynopticSource	VMCv20140428	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20140903	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag5Err	vmcSynopticSource	VMCv20150309	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag5Err	vmcSynopticSource	VMCv20151218	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20160311	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20160822	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20170109	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20170411	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20171101	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20180702	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20181120	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20191212	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20210708	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20230816	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcSynopticSource	VMCv20240226	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcdeepSynopticSource	VMCDEEPv20230713	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vmcdeepSynopticSource	VMCDEEPv20240506	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag5Err	vvvSynopticSource	VVVDR1	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag5Err	vvvSynopticSource	VVVDR2	Error in extended source Ks mag (4.0 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6	sharksSource	SHARKSv20210222	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	sharksSource	SHARKSv20210421	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	ultravistaSource	ULTRAVISTADR4	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Point source Ks aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vhsSource	VHSDR1	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vhsSource	VHSDR2	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vhsSource	VHSDR3	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSDR4	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSDR5	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSDR6	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20120926	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vhsSource	VHSv20130417	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vhsSource	VHSv20140409	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20150108	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20160114	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20160507	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20170630	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20180419	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20201209	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20231101	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vhsSource	VHSv20240731	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	videoSource	VIDEODR2	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	videoSource	VIDEODR3	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	videoSource	VIDEODR4	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	videoSource	VIDEODR5	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	videoSource	VIDEOv20100513	Extended source Ks mag, no aperture correction applied	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	videoSource	VIDEOv20111208	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingSource	VIKINGDR2	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingSource	VIKINGDR3	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingSource	VIKINGDR4	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingSource	VIKINGv20110714	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingSource	VIKINGv20111019	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingSource	VIKINGv20130417	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingSource	VIKINGv20140402	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingSource	VIKINGv20150421	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingSource	VIKINGv20151230	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingSource	VIKINGv20160406	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingSource	VIKINGv20161202	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingSource	VIKINGv20170715	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Point source Ks aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Point source Ks aperture corrected (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCDR1	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCDR2	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCDR3	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCDR4	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCDR5	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20110816	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCv20110909	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCv20120126	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCv20121128	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCv20130304	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMag6	vmcSource	VMCv20130805	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20140428	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20140903	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20150309	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20151218	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20160311	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20160822	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20170109	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20170411	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20171101	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20180702	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20181120	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20191212	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20210708	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20230816	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcSource	VMCv20240226	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcdeepSource	VMCDEEPv20230713	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6	vmcdeepSource	VMCDEEPv20240506	Point source Ks aperture corrected mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMag6Err	sharksSource	SHARKSv20210222	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	sharksSource	SHARKSv20210421	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	ultravistaSource	ULTRAVISTADR4	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in point/extended source Ks (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vhsSource	VHSDR1	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vhsSource	VHSDR2	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vhsSource	VHSDR3	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag6Err	vhsSource	VHSDR4	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	vhsSource	VHSDR5	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSDR6	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20120926	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vhsSource	VHSv20130417	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vhsSource	VHSv20140409	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag6Err	vhsSource	VHSv20150108	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	vhsSource	VHSv20160114	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20160507	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20170630	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20180419	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20201209	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20231101	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vhsSource	VHSv20240731	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	videoSource	VIDEODR2	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	videoSource	VIDEODR3	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	videoSource	VIDEODR4	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	videoSource	VIDEODR5	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	videoSource	VIDEOv20100513	Error in extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	videoSource	VIDEOv20111208	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGDR2	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGDR3	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGDR4	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag6Err	vikingSource	VIKINGv20110714	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGv20111019	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGv20130417	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGv20140402	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingSource	VIKINGv20150421	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	vikingSource	VIKINGv20151230	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vikingSource	VIKINGv20160406	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vikingSource	VIKINGv20161202	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vikingSource	VIKINGv20170715	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Error in point/extended source Ks (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Error in point/extended source Ks (5.7 arcsec aperture diameter) magnitude	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCDR1	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCDR2	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCDR3	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	vmcSource	VMCDR4	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCDR5	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20110816	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCv20110909	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCv20120126	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCv20121128	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCv20130304	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCv20130805	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error
ksAperMag6Err	vmcSource	VMCv20140428	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksAperMag6Err	vmcSource	VMCv20140903	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	vmcSource	VMCv20150309	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksAperMag6Err	vmcSource	VMCv20151218	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20160311	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20160822	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20170109	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20170411	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20171101	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20180702	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20181120	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20191212	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20210708	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20230816	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcSource	VMCv20240226	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcdeepSource	VMCDEEPv20230713	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMag6Err	vmcdeepSource	VMCDEEPv20240506	Error in point/extended source Ks mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksAperMagNoAperCorr3	sharksSource	SHARKSv20210222	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	sharksSource	SHARKSv20210421	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	ultravistaSource	ULTRAVISTADR4	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	ultravistaSourceRemeasurement	ULTRAVISTADR4	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vhsSource	VHSDR1	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vhsSource	VHSDR2	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vhsSource	VHSDR3	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSDR4	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSDR5	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSDR6	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20120926	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vhsSource	VHSv20130417	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vhsSource	VHSv20140409	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20150108	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20160114	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20160507	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20170630	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20180419	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20201209	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20231101	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vhsSource	VHSv20240731	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	videoSource	VIDEODR2	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	videoSource	VIDEODR3	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	videoSource	VIDEODR4	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	videoSource	VIDEODR5	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	videoSource	VIDEOv20111208	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingSource	VIKINGDR2	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingSource	VIKINGDR3	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingSource	VIKINGDR4	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingSource	VIKINGv20110714	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingSource	VIKINGv20111019	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingSource	VIKINGv20130417	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingSource	VIKINGv20140402	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingSource	VIKINGv20150421	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingSource	VIKINGv20151230	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingSource	VIKINGv20160406	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingSource	VIKINGv20161202	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingSource	VIKINGv20170715	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Default extended source Ks (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCDR1	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCDR2	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCDR3	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCDR4	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCDR5	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20110816	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCv20110909	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCv20120126	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCv20121128	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCv20130304	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr3	vmcSource	VMCv20130805	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20140428	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20140903	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20150309	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20151218	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20160311	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20160822	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20170109	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20170411	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20171101	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20180702	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20181120	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20191212	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20210708	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20230816	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcSource	VMCv20240226	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcdeepSource	VMCDEEPv20230713	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr3	vmcdeepSource	VMCDEEPv20240506	Default extended source Ks aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	sharksSource	SHARKSv20210222	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	sharksSource	SHARKSv20210421	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	ultravistaSource	ULTRAVISTADR4	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vhsSource	VHSDR1	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vhsSource	VHSDR2	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vhsSource	VHSDR3	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSDR4	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSDR5	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSDR6	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20120926	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vhsSource	VHSv20130417	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vhsSource	VHSv20140409	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20150108	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20160114	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20160507	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20170630	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20180419	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20201209	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20231101	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vhsSource	VHSv20240731	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	videoSource	VIDEODR2	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	videoSource	VIDEODR3	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	videoSource	VIDEODR4	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	videoSource	VIDEODR5	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	videoSource	VIDEOv20111208	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingSource	VIKINGDR2	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingSource	VIKINGDR3	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingSource	VIKINGDR4	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingSource	VIKINGv20110714	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingSource	VIKINGv20111019	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingSource	VIKINGv20130417	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingSource	VIKINGv20140402	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingSource	VIKINGv20150421	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingSource	VIKINGv20151230	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingSource	VIKINGv20160406	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingSource	VIKINGv20161202	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingSource	VIKINGv20170715	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Ks (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCDR1	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCDR2	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCDR3	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCDR4	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCDR5	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20110816	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCv20110909	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCv20120126	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCv20121128	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCv20130304	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr4	vmcSource	VMCv20130805	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20140428	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20140903	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20150309	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20151218	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20160311	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20160822	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20170109	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20170411	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20171101	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20180702	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20181120	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20191212	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20210708	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20230816	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcSource	VMCv20240226	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcdeepSource	VMCDEEPv20230713	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr4	vmcdeepSource	VMCDEEPv20240506	Extended source Ks aperture mag (2.8 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	sharksSource	SHARKSv20210222	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	sharksSource	SHARKSv20210421	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	ultravistaSource	ULTRAVISTADR4	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vhsSource	VHSDR1	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vhsSource	VHSDR2	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vhsSource	VHSDR3	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSDR4	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSDR5	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSDR6	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20120926	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vhsSource	VHSv20130417	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vhsSource	VHSv20140409	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20150108	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20160114	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20160507	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20170630	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20180419	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20201209	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20231101	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vhsSource	VHSv20240731	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	videoSource	VIDEODR2	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	videoSource	VIDEODR3	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	videoSource	VIDEODR4	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	videoSource	VIDEODR5	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	videoSource	VIDEOv20111208	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingSource	VIKINGDR2	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingSource	VIKINGDR3	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingSource	VIKINGDR4	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingSource	VIKINGv20110714	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingSource	VIKINGv20111019	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingSource	VIKINGv20130417	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingSource	VIKINGv20140402	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingSource	VIKINGv20150421	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingSource	VIKINGv20151230	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingSource	VIKINGv20160406	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingSource	VIKINGv20161202	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingSource	VIKINGv20170715	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Extended source Ks (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCDR1	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCDR2	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCDR3	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCDR4	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCDR5	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20110816	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCv20110909	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCv20120126	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCv20121128	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCv20130304	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag
ksAperMagNoAperCorr6	vmcSource	VMCv20130805	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20140428	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20140903	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20150309	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20151218	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20160311	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20160822	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20170109	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20170411	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20171101	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20180702	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20181120	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20191212	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20210708	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20230816	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcSource	VMCv20240226	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcdeepSource	VMCDEEPv20230713	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksAperMagNoAperCorr6	vmcdeepSource	VMCDEEPv20240506	Extended source Ks aperture mag (5.7 arcsec aperture diameter)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksaStratAst	sharksVarFrameSetInfo	SHARKSv20210222	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	sharksVarFrameSetInfo	SHARKSv20210421	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vvvVarFrameSetInfo	VVVDR1	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vvvVarFrameSetInfo	VVVDR2	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratAst	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vvvVarFrameSetInfo	VVVv20110718	Strateva parameter, a, in fit to astrometric rms vs magnitude in Ks 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.
ksaStratAst	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksaStratPht	sharksVarFrameSetInfo	SHARKSv20210222	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	sharksVarFrameSetInfo	SHARKSv20210421	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vvvVarFrameSetInfo	VVVDR1	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vvvVarFrameSetInfo	VVVDR2	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksaStratPht	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vvvVarFrameSetInfo	VVVv20110718	Strateva parameter, a, in fit to photometric rms vs magnitude in Ks 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.
ksaStratPht	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksAverageConf	sharksSource	SHARKSv20210222	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	sharksSource	SHARKSv20210421	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	ultravistaSourceRemeasurement	ULTRAVISTADR4	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vhsSource	VHSDR1	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vhsSource	VHSDR2	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vhsSource	VHSDR3	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSDR4	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSDR5	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSDR6	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20120926	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	stat.likelihood;em.IR.NIR
ksAverageConf	vhsSource	VHSv20130417	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vhsSource	VHSv20140409	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20150108	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20160114	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20160507	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20170630	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20180419	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20201209	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20231101	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vhsSource	VHSv20240731	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingSource	VIKINGDR2	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vikingSource	VIKINGDR3	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	stat.likelihood;em.IR.NIR
ksAverageConf	vikingSource	VIKINGDR4	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingSource	VIKINGv20110714	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vikingSource	VIKINGv20111019	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vikingSource	VIKINGv20130417	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vikingSource	VIKINGv20140402	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vikingSource	VIKINGv20150421	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingSource	VIKINGv20151230	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingSource	VIKINGv20160406	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingSource	VIKINGv20161202	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingSource	VIKINGv20170715	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vmcSource	VMCDR2	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vmcSource	VMCDR3	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCDR4	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCDR5	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20110816	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vmcSource	VMCv20110909	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vmcSource	VMCv20120126	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vmcSource	VMCv20121128	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	stat.likelihood;em.IR.NIR
ksAverageConf	vmcSource	VMCv20130304	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vmcSource	VMCv20130805	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vmcSource	VMCv20140428	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20140903	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20150309	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20151218	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20160311	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20160822	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20170109	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20170411	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20171101	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20180702	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20181120	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20191212	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20210708	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20230816	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource	VMCv20240226	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcSource, vmcSynopticSource	VMCDR1	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	meta.code
ksAverageConf	vmcdeepSource	VMCDEEPv20240506	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vvvSource	VVVDR2	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.NIR
ksAverageConf	vvvSource	VVVDR5	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksAverageConf	vvvSource, vvvSynopticSource	VVVDR1	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-99999999	stat.likelihood;em.IR.NIR
ksAverageConf	vvvxSource	VVVXDR1	average confidence in 2 arcsec diameter default aperture (aper3) Ks	real	4		-0.9999995e9	stat.likelihood;em.IR.K
ksbestAper	sharksVariability	SHARKSv20210222	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	sharksVariability	SHARKSv20210421	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	ultravistaMapLcVariability	ULTRAVISTADR4	Best aperture (1-3) for photometric statistics in the Ks 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)
ksbestAper	ultravistaVariability	ULTRAVISTADR4	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	videoVariability	VIDEODR2	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	videoVariability	VIDEODR3	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	videoVariability	VIDEODR4	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	videoVariability	VIDEODR5	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	videoVariability	VIDEOv20100513	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	videoVariability	VIDEOv20111208	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vikingVariability	VIKINGDR2	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vikingVariability	VIKINGv20110714	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vikingVariability	VIKINGv20111019	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCDR1	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCDR2	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCDR3	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCDR4	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCDR5	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20110816	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCv20110909	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCv20120126	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCv20121128	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCv20130304	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCv20130805	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vmcVariability	VMCv20140428	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20140903	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20150309	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20151218	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20160311	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20160822	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20170109	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20170411	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20171101	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20180702	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20181120	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20191212	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20210708	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20230816	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcVariability	VMCv20240226	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcdeepVariability	VMCDEEPv20230713	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vmcdeepVariability	VMCDEEPv20240506	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vvvVariability	VVVDR1	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vvvVariability	VVVDR2	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vvvVariability	VVVDR5	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbestAper	vvvVariability	VVVv20100531	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vvvVariability	VVVv20110718	Best aperture (1-6) for photometric statistics in the Ks 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)
ksbestAper	vvvxVariability	VVVXDR1	Best aperture (1-6) for photometric statistics in the Ks band	int	4		-9999	meta.code.class;em.IR.K
			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)
ksbStratAst	sharksVarFrameSetInfo	SHARKSv20210222	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	sharksVarFrameSetInfo	SHARKSv20210421	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vvvVarFrameSetInfo	VVVDR1	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vvvVarFrameSetInfo	VVVDR2	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratAst	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vvvVarFrameSetInfo	VVVv20110718	Strateva parameter, b, in fit to astrometric rms vs magnitude in Ks 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.
ksbStratAst	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksbStratPht	sharksVarFrameSetInfo	SHARKSv20210222	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	sharksVarFrameSetInfo	SHARKSv20210421	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vvvVarFrameSetInfo	VVVDR1	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vvvVarFrameSetInfo	VVVDR2	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksbStratPht	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vvvVarFrameSetInfo	VVVv20110718	Strateva parameter, b, in fit to photometric rms vs magnitude in Ks 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.
ksbStratPht	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqAst	sharksVarFrameSetInfo	SHARKSv20210222	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	sharksVarFrameSetInfo	SHARKSv20210421	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	videoVarFrameSetInfo	VIDEODR2	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	videoVarFrameSetInfo	VIDEODR3	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	videoVarFrameSetInfo	VIDEODR4	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	videoVarFrameSetInfo	VIDEODR5	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	videoVarFrameSetInfo	VIDEOv20100513	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	videoVarFrameSetInfo	VIDEOv20111208	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vikingVarFrameSetInfo	VIKINGDR2	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vikingVarFrameSetInfo	VIKINGv20110714	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vikingVarFrameSetInfo	VIKINGv20111019	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCDR1	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCDR2	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCDR3	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCDR4	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCDR5	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20110816	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCv20110909	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCv20120126	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCv20121128	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCv20130304	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCv20130805	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vmcVarFrameSetInfo	VMCv20140428	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20140903	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20150309	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20151218	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20160311	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20160822	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20170109	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20170411	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20171101	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20180702	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20181120	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20191212	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20210708	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20230816	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcVarFrameSetInfo	VMCv20240226	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vvvVarFrameSetInfo	VVVDR1	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vvvVarFrameSetInfo	VVVDR2	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vvvVarFrameSetInfo	VVVDR5	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqAst	vvvVarFrameSetInfo	VVVv20100531	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vvvVarFrameSetInfo	VVVv20110718	Goodness of fit of Strateva function to astrometric data in Ks 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.
kschiSqAst	vvvxVarFrameSetInfo	VVVXDR1	Goodness of fit of Strateva function to astrometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kschiSqpd	sharksVariability	SHARKSv20210222	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	sharksVariability	SHARKSv20210421	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	ultravistaMapLcVariability	ULTRAVISTADR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	ultravistaVariability	ULTRAVISTADR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	videoVariability	VIDEODR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	videoVariability	VIDEODR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	vikingVariability	VIKINGDR2	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	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.
kschiSqpd	vikingVariability	VIKINGv20111019	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	vmcVariability	VMCDR3	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCDR4	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCDR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	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.
kschiSqpd	vmcVariability	VMCv20140428	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20140903	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20150309	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20151218	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20160311	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20160822	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20170109	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20170411	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20171101	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20180702	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20181120	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20191212	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20210708	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20230816	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcVariability	VMCv20240226	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcdeepVariability	VMCDEEPv20230713	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vmcdeepVariability	VMCDEEPv20240506	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vvvVariability	VVVDR1	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.
kschiSqpd	vvvVariability	VVVDR2	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.
kschiSqpd	vvvVariability	VVVDR5	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vvvVariability	VVVv20100531	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqpd	vvvVariability	VVVv20110718	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.
kschiSqpd	vvvxVariability	VVVXDR1	Chi square (per degree of freedom) fit to data (mean and expected rms)	real	4		-0.9999995e9	stat.fit.chi2;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kschiSqPht	sharksVarFrameSetInfo	SHARKSv20210222	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	sharksVarFrameSetInfo	SHARKSv20210421	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	videoVarFrameSetInfo	VIDEODR2	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	videoVarFrameSetInfo	VIDEODR3	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	videoVarFrameSetInfo	VIDEODR4	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	videoVarFrameSetInfo	VIDEODR5	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	videoVarFrameSetInfo	VIDEOv20100513	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	videoVarFrameSetInfo	VIDEOv20111208	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vikingVarFrameSetInfo	VIKINGDR2	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vikingVarFrameSetInfo	VIKINGv20110714	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vikingVarFrameSetInfo	VIKINGv20111019	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCDR1	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCDR2	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCDR3	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCDR4	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCDR5	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20110816	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCv20110909	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCv20120126	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCv20121128	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCv20130304	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCv20130805	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vmcVarFrameSetInfo	VMCv20140428	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20140903	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20150309	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20151218	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20160311	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20160822	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20170109	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20170411	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20171101	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20180702	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20181120	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20191212	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20210708	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20230816	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcVarFrameSetInfo	VMCv20240226	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vvvVarFrameSetInfo	VVVDR1	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vvvVarFrameSetInfo	VVVDR2	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vvvVarFrameSetInfo	VVVDR5	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kschiSqPht	vvvVarFrameSetInfo	VVVv20100531	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vvvVarFrameSetInfo	VVVv20110718	Goodness of fit of Strateva function to photometric data in Ks 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.
kschiSqPht	vvvxVarFrameSetInfo	VVVXDR1	Goodness of fit of Strateva function to photometric data in Ks band	real	4		-0.9999995e9	stat.fit.goodness;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksClass	sharksSource	SHARKSv20210222	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	sharksSource	SHARKSv20210421	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	ultravistaSource	ULTRAVISTADR4	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	ultravistaSourceRemeasurement	ULTRAVISTADR4	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vhsSource	VHSDR2	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vhsSource	VHSDR3	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSDR4	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSDR5	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSDR6	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20120926	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vhsSource	VHSv20130417	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vhsSource	VHSv20140409	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20150108	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20160114	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20160507	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20170630	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20180419	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20201209	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20231101	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource	VHSv20240731	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vhsSource, vhsSourceRemeasurement	VHSDR1	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	videoSource	VIDEODR2	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	videoSource	VIDEODR3	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	videoSource	VIDEODR4	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	videoSource	VIDEODR5	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	videoSource	VIDEOv20111208	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	videoSource, videoSourceRemeasurement	VIDEOv20100513	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingSource	VIKINGDR2	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingSource	VIKINGDR3	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingSource	VIKINGDR4	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vikingSource	VIKINGv20111019	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingSource	VIKINGv20130417	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingSource	VIKINGv20140402	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingSource	VIKINGv20150421	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vikingSource	VIKINGv20151230	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vikingSource	VIKINGv20160406	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vikingSource	VIKINGv20161202	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vikingSource	VIKINGv20170715	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCDR2	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCDR3	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCDR4	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCDR5	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20110909	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCv20120126	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCv20121128	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCv20130304	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCv20130805	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource	VMCv20140428	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20140903	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20150309	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20151218	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20160311	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20160822	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20170109	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20170411	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20171101	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20180702	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20181120	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20191212	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20210708	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20230816	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource	VMCv20240226	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcSource, vmcSourceRemeasurement	VMCv20110816	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcSource, vmcSynopticSource	VMCDR1	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vmcdeepSource	VMCDEEPv20240506	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vvvSource	VVVDR2	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vvvSource	VVVDR5	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClass	vvvSource	VVVv20110718	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vvvSource, vvvSourceRemeasurement	VVVv20100531	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vvvSource, vvvSynopticSource	VVVDR1	discrete image classification flag in Ks	smallint	2		-9999	src.class
ksClass	vvvxSource	VVVXDR1	discrete image classification flag in Ks	smallint	2		-9999	src.class;em.IR.K
ksClassStat	sharksSource	SHARKSv20210222	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	sharksSource	SHARKSv20210421	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	ultravistaSource	ULTRAVISTADR4	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	ultravistaSourceRemeasurement	ULTRAVISTADR4	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vhsSource	VHSDR2	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vhsSource	VHSDR3	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSDR4	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSDR5	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSDR6	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20120926	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vhsSource	VHSv20130417	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vhsSource	VHSv20140409	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20150108	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20160114	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20160507	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20170630	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20180419	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20201209	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20231101	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource	VHSv20240731	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vhsSource, vhsSourceRemeasurement	VHSDR1	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	videoSource	VIDEODR2	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	videoSource	VIDEODR3	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	videoSource	VIDEODR4	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	videoSource	VIDEODR5	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	videoSource	VIDEOv20100513	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	videoSource	VIDEOv20111208	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	videoSourceRemeasurement	VIDEOv20100513	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingSource	VIKINGDR2	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingSource	VIKINGDR3	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingSource	VIKINGDR4	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vikingSource	VIKINGv20111019	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingSource	VIKINGv20130417	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingSource	VIKINGv20140402	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingSource	VIKINGv20150421	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vikingSource	VIKINGv20151230	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vikingSource	VIKINGv20160406	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vikingSource	VIKINGv20161202	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vikingSource	VIKINGv20170715	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCDR2	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCDR3	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCDR4	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCDR5	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20110909	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCv20120126	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCv20121128	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCv20130304	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCv20130805	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource	VMCv20140428	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20140903	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20150309	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20151218	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20160311	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20160822	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20170109	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20170411	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20171101	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20180702	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20181120	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20191212	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20210708	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20230816	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource	VMCv20240226	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcSource, vmcSourceRemeasurement	VMCv20110816	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcSource, vmcSynopticSource	VMCDR1	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vmcdeepSource	VMCDEEPv20240506	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vvvSource	VVVDR1	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSource	VVVDR2	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSource	VVVDR5	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
ksClassStat	vvvSource	VVVv20100531	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSource	VVVv20110718	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSourceRemeasurement	VVVv20100531	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSourceRemeasurement	VVVv20110718	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSynopticSource	VVVDR1	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvSynopticSource	VVVDR2	N(0,1) stellarness-of-profile statistic in Ks	real	4		-0.9999995e9	stat
ksClassStat	vvvxSource	VVVXDR1	S-Extractor classification statistic in Ks	real	4		-0.9999995e9	stat;em.IR.K
kscStratAst	sharksVarFrameSetInfo	SHARKSv20210222	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	sharksVarFrameSetInfo	SHARKSv20210421	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20180702	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20181120	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20191212	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20210708	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20230816	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcVarFrameSetInfo	VMCv20240226	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vvvVarFrameSetInfo	VVVDR1	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vvvVarFrameSetInfo	VVVDR2	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vvvVarFrameSetInfo	VVVDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratAst	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vvvVarFrameSetInfo	VVVv20110718	Strateva parameter, c, in fit to astrometric rms vs magnitude in Ks 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.
kscStratAst	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
kscStratPht	sharksVarFrameSetInfo	SHARKSv20210222	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	sharksVarFrameSetInfo	SHARKSv20210421	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	ultravistaMapLcVarFrameSetInfo	ULTRAVISTADR4	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	videoVarFrameSetInfo	VIDEODR2	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	videoVarFrameSetInfo	VIDEODR3	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	videoVarFrameSetInfo	VIDEODR4	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	videoVarFrameSetInfo	VIDEODR5	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	videoVarFrameSetInfo	VIDEOv20100513	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	videoVarFrameSetInfo	VIDEOv20111208	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vikingVarFrameSetInfo	VIKINGDR2	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vikingVarFrameSetInfo	VIKINGv20110714	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vikingVarFrameSetInfo	VIKINGv20111019	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCDR1	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCDR2	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCDR3	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCDR4	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20110816	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCv20110909	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCv20120126	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCv20121128	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCv20130304	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCv20130805	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vmcVarFrameSetInfo	VMCv20140428	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20140903	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20150309	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20151218	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20160311	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20160822	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20170109	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20170411	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20171101	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks band, see Sesar et al. 2007.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20180702	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20181120	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20191212	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20210708	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20230816	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcVarFrameSetInfo	VMCv20240226	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vvvVarFrameSetInfo	VVVDR1	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vvvVarFrameSetInfo	VVVDR2	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vvvVarFrameSetInfo	VVVDR5	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
kscStratPht	vvvVarFrameSetInfo	VVVv20100531	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vvvVarFrameSetInfo	VVVv20110718	Strateva parameter, c, in fit to photometric rms vs magnitude in Ks 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.
kscStratPht	vvvxVarFrameSetInfo	VVVXDR1	Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Ks band.	real	4		-0.9999995e9	stat.fit.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksDeblend	vhsSourceRemeasurement	VHSDR1	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	videoSource, videoSourceRemeasurement	VIDEOv20100513	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	vikingSourceRemeasurement	VIKINGv20110714	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	vikingSourceRemeasurement	VIKINGv20111019	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	vmcSourceRemeasurement	VMCv20110816	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	vmcSourceRemeasurement	VMCv20110909	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	vvvSource	VVVv20110718	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksDeblend	vvvSource, vvvSourceRemeasurement	VVVv20100531	placeholder flag indicating parent/child relation in Ks	int	4		-99999999	meta.code
ksEll	sharksSource	SHARKSv20210222	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	sharksSource	SHARKSv20210421	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	ultravistaSource	ULTRAVISTADR4	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	ultravistaSourceRemeasurement	ULTRAVISTADR4	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticty
ksEll	vhsSource	VHSDR2	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vhsSource	VHSDR3	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSDR4	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSDR5	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSDR6	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20120926	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vhsSource	VHSv20130417	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vhsSource	VHSv20140409	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20150108	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20160114	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20160507	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20170630	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20180419	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20201209	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20231101	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource	VHSv20240731	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vhsSource, vhsSourceRemeasurement	VHSDR1	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	videoSource	VIDEODR2	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	videoSource	VIDEODR3	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	videoSource	VIDEODR4	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	videoSource	VIDEODR5	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	videoSource	VIDEOv20111208	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	videoSource, videoSourceRemeasurement	VIDEOv20100513	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingSource	VIKINGDR2	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingSource	VIKINGDR3	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingSource	VIKINGDR4	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vikingSource	VIKINGv20111019	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingSource	VIKINGv20130417	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingSource	VIKINGv20140402	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingSource	VIKINGv20150421	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vikingSource	VIKINGv20151230	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vikingSource	VIKINGv20160406	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vikingSource	VIKINGv20161202	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vikingSource	VIKINGv20170715	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCDR2	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCDR3	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCDR4	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCDR5	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20110909	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCv20120126	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCv20121128	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCv20130304	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCv20130805	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource	VMCv20140428	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20140903	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20150309	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20151218	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20160311	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20160822	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20170109	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20170411	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20171101	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20180702	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20181120	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20191212	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20210708	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20230816	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource	VMCv20240226	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcSource, vmcSourceRemeasurement	VMCv20110816	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcSource, vmcSynopticSource	VMCDR1	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vmcdeepSource	VMCDEEPv20240506	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vvvSource	VVVDR2	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vvvSource	VVVDR5	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
ksEll	vvvSource	VVVv20110718	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vvvSource, vvvSourceRemeasurement	VVVv20100531	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vvvSource, vvvSynopticSource	VVVDR1	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity
ksEll	vvvxSource	VVVXDR1	1-b/a, where a/b=semi-major/minor axes in Ks	real	4		-0.9999995e9	src.ellipticity;em.IR.K
kseNum	sharksMergeLog	SHARKSv20210222	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	sharksMergeLog	SHARKSv20210421	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	ultravistaMergeLog, ultravistaRemeasMergeLog	ULTRAVISTADR4	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSDR1	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vhsMergeLog	VHSDR2	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vhsMergeLog	VHSDR3	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSDR4	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSDR5	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSDR6	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20120926	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vhsMergeLog	VHSv20130417	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vhsMergeLog	VHSv20140409	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20150108	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20160114	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20160507	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20170630	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20180419	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vhsMergeLog	VHSv20201209	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vhsMergeLog	VHSv20231101	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vhsMergeLog	VHSv20240731	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	videoMergeLog	VIDEODR2	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	videoMergeLog	VIDEODR3	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	videoMergeLog	VIDEODR4	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	videoMergeLog	VIDEODR5	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	videoMergeLog	VIDEOv20100513	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	videoMergeLog	VIDEOv20111208	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGDR2	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGDR3	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGDR4	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vikingMergeLog	VIKINGv20110714	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGv20111019	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGv20130417	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGv20140402	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingMergeLog	VIKINGv20150421	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vikingMergeLog	VIKINGv20151230	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vikingMergeLog	VIKINGv20160406	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vikingMergeLog	VIKINGv20161202	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vikingMergeLog	VIKINGv20170715	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20170124	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCDR2	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCDR3	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCDR4	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCDR5	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vmcMergeLog	VMCv20110816	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCv20110909	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCv20120126	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCv20121128	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCv20130304	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCv20130805	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcMergeLog	VMCv20140428	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20140903	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20150309	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20151218	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20160311	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20160822	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20170109	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20170411	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20171101	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20180702	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20181120	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vmcMergeLog	VMCv20191212	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vmcMergeLog	VMCv20210708	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vmcMergeLog	VMCv20230816	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vmcMergeLog	VMCv20240226	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vmcMergeLog, vmcSynopticMergeLog	VMCDR1	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vmcdeepMergeLog	VMCDEEPv20240506	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vmcdeepMergeLog, vmcdeepSynopticMergeLog	VMCDEEPv20230713	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
kseNum	vvvMergeLog	VVVDR2	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vvvMergeLog	VVVv20100531	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vvvMergeLog	VVVv20110718	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vvvMergeLog, vvvPsfDaophotJKsMergeLog	VVVDR5	the extension number of this Ks frame	tinyint	1			meta.number;em.IR.K
kseNum	vvvMergeLog, vvvSynopticMergeLog	VVVDR1	the extension number of this Ks frame	tinyint	1			meta.number
kseNum	vvvxMergeLog	VVVXDR1	the extension number of this Ks frame	tinyint	1			meta.id;em.IR.K
ksErrBits	sharksSource	SHARKSv20210222	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	sharksSource	SHARKSv20210421	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	ultravistaSource	ULTRAVISTADR4	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			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
ksErrBits	ultravistaSourceRemeasurement	ULTRAVISTADR4	processing warning/error bitwise flags in Ks	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.
ksErrBits	vhsSource	VHSDR1	processing warning/error bitwise flags in Ks	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.
ksErrBits	vhsSource	VHSDR2	processing warning/error bitwise flags in Ks	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.
ksErrBits	vhsSource	VHSDR3	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSDR4	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSDR5	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSDR6	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20120926	processing warning/error bitwise flags in Ks	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.
ksErrBits	vhsSource	VHSv20130417	processing warning/error bitwise flags in Ks	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.
ksErrBits	vhsSource	VHSv20140409	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20150108	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20160114	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20160507	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20170630	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20180419	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20201209	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20231101	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSource	VHSv20240731	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vhsSourceRemeasurement	VHSDR1	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	videoSource	VIDEODR2	processing warning/error bitwise flags in Ks	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
ksErrBits	videoSource	VIDEODR3	processing warning/error bitwise flags in Ks	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
ksErrBits	videoSource	VIDEODR4	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			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
ksErrBits	videoSource	VIDEODR5	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			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
ksErrBits	videoSource	VIDEOv20100513	processing warning/error bitwise flags in Ks	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
ksErrBits	videoSource	VIDEOv20111208	processing warning/error bitwise flags in Ks	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
ksErrBits	videoSourceRemeasurement	VIDEOv20100513	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vikingSource	VIKINGDR2	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingSource	VIKINGDR3	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingSource	VIKINGDR4	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vikingSource	VIKINGv20110714	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingSource	VIKINGv20111019	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingSource	VIKINGv20130417	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingSource	VIKINGv20140402	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingSource	VIKINGv20150421	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vikingSource	VIKINGv20151230	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vikingSource	VIKINGv20160406	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vikingSource	VIKINGv20161202	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vikingSource	VIKINGv20170715	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vikingSourceRemeasurement	VIKINGv20110714	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vikingSourceRemeasurement	VIKINGv20111019	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	processing warning/error bitwise flags in Ks	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.
ksErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCDR2	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCDR3	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCDR4	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCDR5	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20110816	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCv20110909	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCv20120126	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCv20121128	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCv20130304	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCv20130805	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSource	VMCv20140428	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20140903	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20150309	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20151218	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20160311	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20160822	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20170109	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20170411	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20171101	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20180702	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20181120	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20191212	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20210708	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20230816	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource	VMCv20240226	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcSource, vmcSynopticSource	VMCDR1	processing warning/error bitwise flags in Ks	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.
ksErrBits	vmcSourceRemeasurement	VMCv20110816	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vmcSourceRemeasurement	VMCv20110909	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vmcdeepSource	VMCDEEPv20240506	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vvvSource	VVVDR2	processing warning/error bitwise flags in Ks	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.
ksErrBits	vvvSource	VVVDR5	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksErrBits	vvvSource	VVVv20100531	processing warning/error bitwise flags in Ks	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.
ksErrBits	vvvSource	VVVv20110718	processing warning/error bitwise flags in Ks	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.
ksErrBits	vvvSource, vvvSynopticSource	VVVDR1	processing warning/error bitwise flags in Ks	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.
ksErrBits	vvvSourceRemeasurement	VVVv20100531	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vvvSourceRemeasurement	VVVv20110718	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code
ksErrBits	vvvxSource	VVVXDR1	processing warning/error bitwise flags in Ks	int	4		-99999999	meta.code;em.IR.K
			Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
ksEta	sharksSource	SHARKSv20210222	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	sharksSource	SHARKSv20210421	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	ultravistaSource	ULTRAVISTADR4	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSDR1	Offset of Ks 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.
ksEta	vhsSource	VHSDR2	Offset of Ks 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.
ksEta	vhsSource	VHSDR3	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSDR4	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSDR5	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSDR6	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20120926	Offset of Ks 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.
ksEta	vhsSource	VHSv20130417	Offset of Ks 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.
ksEta	vhsSource	VHSv20140409	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20150108	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20160114	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20160507	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20170630	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20180419	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20201209	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20231101	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vhsSource	VHSv20240731	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	videoSource	VIDEODR2	Offset of Ks 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.
ksEta	videoSource	VIDEODR3	Offset of Ks 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.
ksEta	videoSource	VIDEODR4	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	videoSource	VIDEODR5	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	videoSource	VIDEOv20100513	Offset of Ks 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.
ksEta	videoSource	VIDEOv20111208	Offset of Ks 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.
ksEta	vikingSource	VIKINGDR2	Offset of Ks 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.
ksEta	vikingSource	VIKINGDR3	Offset of Ks 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.
ksEta	vikingSource	VIKINGDR4	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vikingSource	VIKINGv20110714	Offset of Ks 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.
ksEta	vikingSource	VIKINGv20111019	Offset of Ks 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.
ksEta	vikingSource	VIKINGv20130417	Offset of Ks 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.
ksEta	vikingSource	VIKINGv20140402	Offset of Ks 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.
ksEta	vikingSource	VIKINGv20150421	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vikingSource	VIKINGv20151230	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vikingSource	VIKINGv20160406	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vikingSource	VIKINGv20161202	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vikingSource	VIKINGv20170715	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCDR2	Offset of Ks 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.
ksEta	vmcSource	VMCDR3	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCDR4	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCDR5	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20110816	Offset of Ks 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.
ksEta	vmcSource	VMCv20110909	Offset of Ks 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.
ksEta	vmcSource	VMCv20120126	Offset of Ks 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.
ksEta	vmcSource	VMCv20121128	Offset of Ks 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.
ksEta	vmcSource	VMCv20130304	Offset of Ks 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.
ksEta	vmcSource	VMCv20130805	Offset of Ks 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.
ksEta	vmcSource	VMCv20140428	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20140903	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20150309	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20151218	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20160311	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20160822	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20170109	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20170411	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20171101	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20180702	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20181120	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20191212	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20210708	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20230816	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource	VMCv20240226	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcSource, vmcSynopticSource	VMCDR1	Offset of Ks 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.
ksEta	vmcdeepSource	VMCDEEPv20240506	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vvvSource	VVVDR2	Offset of Ks 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.
ksEta	vvvSource	VVVDR5	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksEta	vvvSource	VVVv20100531	Offset of Ks 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.
ksEta	vvvSource	VVVv20110718	Offset of Ks 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.
ksEta	vvvSource, vvvSynopticSource	VVVDR1	Offset of Ks 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.
ksEta	vvvxSource	VVVXDR1	Offset of Ks detection from master position (+north/-south)	real	4	arcsec	-0.9999995e9	pos.eq.dec;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksexpML	sharksVarFrameSetInfo	SHARKSv20210222	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	sharksVarFrameSetInfo	SHARKSv20210421	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	videoVarFrameSetInfo	VIDEODR2	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	videoVarFrameSetInfo	VIDEODR3	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9	phot.mag;stat.max;em.IR.NIR
ksexpML	videoVarFrameSetInfo	VIDEODR4	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	videoVarFrameSetInfo	VIDEODR5	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	videoVarFrameSetInfo	VIDEOv20100513	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	videoVarFrameSetInfo	VIDEOv20111208	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vikingVarFrameSetInfo	VIKINGDR2	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vikingVarFrameSetInfo	VIKINGv20110714	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vikingVarFrameSetInfo	VIKINGv20111019	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vmcVarFrameSetInfo	VMCDR1	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vmcVarFrameSetInfo	VMCDR2	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max;em.IR.NIR
ksexpML	vmcVarFrameSetInfo	VMCDR3	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCDR4	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCDR5	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20110816	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vmcVarFrameSetInfo	VMCv20110909	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vmcVarFrameSetInfo	VMCv20120126	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vmcVarFrameSetInfo	VMCv20121128	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
ksexpML	vmcVarFrameSetInfo	VMCv20130304	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
ksexpML	vmcVarFrameSetInfo	VMCv20130805	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max;em.IR.NIR
ksexpML	vmcVarFrameSetInfo	VMCv20140428	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20140903	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20150309	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20151218	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20160311	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20160822	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20170109	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20170411	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20171101	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20180702	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20181120	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20191212	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20210708	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20230816	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcVarFrameSetInfo	VMCv20240226	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vvvVarFrameSetInfo	VVVDR1	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;stat.max;em.IR.NIR
ksexpML	vvvVarFrameSetInfo	VVVDR2	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max;em.IR.NIR
ksexpML	vvvVarFrameSetInfo	VVVDR5	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksexpML	vvvVarFrameSetInfo	VVVv20100531	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vvvVarFrameSetInfo	VVVv20110718	Expected magnitude limit of frameSet in this in Ks band.	real	4		-0.9999995e9
ksexpML	vvvxVarFrameSetInfo	VVVXDR1	Expected magnitude limit of frameSet in this in Ks band.	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.max
ksExpRms	sharksVariability	SHARKSv20210222	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	sharksVariability	SHARKSv20210421	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	ultravistaMapLcVariability	ULTRAVISTADR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	ultravistaVariability	ULTRAVISTADR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	videoVariability	VIDEODR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	videoVariability	VIDEODR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	videoVariability	VIDEODR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	videoVariability	VIDEODR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	videoVariability	VIDEOv20100513	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	videoVariability	VIDEOv20111208	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vikingVariability	VIKINGDR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vikingVariability	VIKINGv20110714	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vikingVariability	VIKINGv20111019	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCDR1	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCDR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCDR3	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCDR4	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCDR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20110816	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCv20110909	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCv20120126	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCv20121128	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCv20130304	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCv20130805	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vmcVariability	VMCv20140428	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20140903	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20150309	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20151218	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20160311	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20160822	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20170109	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20170411	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20171101	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20180702	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20181120	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20191212	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20210708	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20230816	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcVariability	VMCv20240226	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcdeepVariability	VMCDEEPv20230713	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vmcdeepVariability	VMCDEEPv20240506	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vvvVariability	VVVDR1	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vvvVariability	VVVDR2	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vvvVariability	VVVDR5	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksExpRms	vvvVariability	VVVv20100531	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vvvVariability	VVVv20110718	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks 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.
ksExpRms	vvvxVariability	VVVXDR1	Rms calculated from polynomial fit to modal RMS as a function of magnitude in Ks band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksGausig	sharksSource	SHARKSv20210222	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	sharksSource	SHARKSv20210421	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	ultravistaSource	ULTRAVISTADR4	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	ultravistaSourceRemeasurement	ULTRAVISTADR4	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vhsSource	VHSDR2	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vhsSource	VHSDR3	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSDR4	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSDR5	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSDR6	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20120926	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vhsSource	VHSv20130417	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vhsSource	VHSv20140409	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20150108	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20160114	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20160507	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20170630	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20180419	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20201209	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20231101	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource	VHSv20240731	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vhsSource, vhsSourceRemeasurement	VHSDR1	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	videoSource	VIDEODR2	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	videoSource	VIDEODR3	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	videoSource	VIDEODR4	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	videoSource	VIDEODR5	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	videoSource	VIDEOv20111208	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	videoSource, videoSourceRemeasurement	VIDEOv20100513	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingSource	VIKINGDR2	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingSource	VIKINGDR3	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingSource	VIKINGDR4	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vikingSource	VIKINGv20111019	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingSource	VIKINGv20130417	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingSource	VIKINGv20140402	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingSource	VIKINGv20150421	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vikingSource	VIKINGv20151230	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vikingSource	VIKINGv20160406	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vikingSource	VIKINGv20161202	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vikingSource	VIKINGv20170715	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCDR2	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCDR3	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCDR4	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCDR5	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20110909	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCv20120126	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCv20121128	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCv20130304	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCv20130805	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource	VMCv20140428	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20140903	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20150309	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20151218	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20160311	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20160822	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20170109	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20170411	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20171101	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20180702	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20181120	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20191212	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20210708	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20230816	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource	VMCv20240226	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcSource, vmcSourceRemeasurement	VMCv20110816	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcSource, vmcSynopticSource	VMCDR1	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vmcdeepSource	VMCDEEPv20240506	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vvvSource	VVVDR2	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vvvSource	VVVDR5	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksGausig	vvvSource	VVVv20110718	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vvvSource, vvvSourceRemeasurement	VVVv20100531	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vvvSource, vvvSynopticSource	VVVDR1	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param
ksGausig	vvvxSource	VVVXDR1	RMS of axes of ellipse fit in Ks	real	4	pixels	-0.9999995e9	src.morph.param;em.IR.K
ksHalfRad	ultravistaSource	ULTRAVISTADR4	SExtractor half-light radius in Ks band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.K
ksHalfRad	videoSource	VIDEODR4	SExtractor half-light radius in Ks band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.K
ksHalfRad	videoSource	VIDEODR5	SExtractor half-light radius in Ks band	real	4	pixels	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	sharksSource	SHARKSv20210222	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	sharksSource	SHARKSv20210421	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	ultravistaSource	ULTRAVISTADR4	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	ultravistaSourceRemeasurement	ULTRAVISTADR4	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	vhsSource	VHSDR1	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	vhsSource	VHSDR2	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	vhsSource	VHSDR3	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSDR4	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSDR5	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSDR6	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20120926	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	vhsSource	VHSv20130417	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	vhsSource	VHSv20140409	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20150108	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20160114	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20160507	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20170630	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20180419	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20201209	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20231101	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vhsSource	VHSv20240731	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	videoSource	VIDEODR2	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	videoSource	VIDEODR3	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	videoSource	VIDEODR4	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	videoSource	VIDEODR5	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	videoSource	VIDEOv20100513	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	videoSource	VIDEOv20111208	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	vikingSource	VIKINGDR2	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	vikingSource	VIKINGDR3	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	vikingSource	VIKINGDR4	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vikingSource	VIKINGv20110714	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	vikingSource	VIKINGv20111019	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;src
ksHlCorSMjRadAs	vikingSource	VIKINGv20130417	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	vikingSource	VIKINGv20140402	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize
ksHlCorSMjRadAs	vikingSource	VIKINGv20150421	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vikingSource	VIKINGv20151230	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vikingSource	VIKINGv20160406	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vikingSource	VIKINGv20161202	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksHlCorSMjRadAs	vikingSource	VIKINGv20170715	Seeing corrected half-light, semi-major axis in Ks band	real	4	arcsec	-0.9999995e9	phys.angSize;em.IR.K
ksIntRms	sharksVariability	SHARKSv20210222	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	sharksVariability	SHARKSv20210421	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	ultravistaMapLcVariability	ULTRAVISTADR4	Intrinsic rms in Ks-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.
ksIntRms	ultravistaVariability	ULTRAVISTADR4	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	videoVariability	VIDEODR2	Intrinsic rms in Ks-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.
ksIntRms	videoVariability	VIDEODR3	Intrinsic rms in Ks-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.
ksIntRms	videoVariability	VIDEODR4	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	videoVariability	VIDEODR5	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	videoVariability	VIDEOv20100513	Intrinsic rms in Ks-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.
ksIntRms	videoVariability	VIDEOv20111208	Intrinsic rms in Ks-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.
ksIntRms	vikingVariability	VIKINGDR2	Intrinsic rms in Ks-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.
ksIntRms	vikingVariability	VIKINGv20110714	Intrinsic rms in Ks-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.
ksIntRms	vikingVariability	VIKINGv20111019	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCDR1	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCDR2	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCDR3	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCDR4	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCDR5	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20110816	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCv20110909	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCv20120126	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCv20121128	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCv20130304	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCv20130805	Intrinsic rms in Ks-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.
ksIntRms	vmcVariability	VMCv20140428	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20140903	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20150309	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20151218	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20160311	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20160822	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20170109	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20170411	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20171101	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20180702	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20181120	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20191212	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20210708	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20230816	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcVariability	VMCv20240226	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcdeepVariability	VMCDEEPv20230713	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vmcdeepVariability	VMCDEEPv20240506	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vvvVariability	VVVDR1	Intrinsic rms in Ks-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.
ksIntRms	vvvVariability	VVVDR2	Intrinsic rms in Ks-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.
ksIntRms	vvvVariability	VVVDR5	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksIntRms	vvvVariability	VVVv20100531	Intrinsic rms in Ks-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.
ksIntRms	vvvVariability	VVVv20110718	Intrinsic rms in Ks-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.
ksIntRms	vvvxVariability	VVVXDR1	Intrinsic rms in Ks-band	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksisDefAst	sharksVarFrameSetInfo	SHARKSv20210222	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	sharksVarFrameSetInfo	SHARKSv20210421	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	videoVarFrameSetInfo	VIDEODR2	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	videoVarFrameSetInfo	VIDEODR3	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	videoVarFrameSetInfo	VIDEODR4	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	videoVarFrameSetInfo	VIDEODR5	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	videoVarFrameSetInfo	VIDEOv20111208	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vikingVarFrameSetInfo	VIKINGDR2	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vikingVarFrameSetInfo	VIKINGv20111019	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vmcVarFrameSetInfo	VMCDR1	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vmcVarFrameSetInfo	VMCDR2	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	vmcVarFrameSetInfo	VMCDR3	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCDR4	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCDR5	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20110816	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vmcVarFrameSetInfo	VMCv20110909	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vmcVarFrameSetInfo	VMCv20120126	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vmcVarFrameSetInfo	VMCv20121128	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	vmcVarFrameSetInfo	VMCv20130304	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	vmcVarFrameSetInfo	VMCv20130805	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	vmcVarFrameSetInfo	VMCv20140428	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20140903	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20150309	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20151218	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20160311	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20160822	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20170109	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20170411	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20171101	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20180702	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20181120	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20191212	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20210708	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20230816	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcVarFrameSetInfo	VMCv20240226	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vvvVarFrameSetInfo	VVVDR1	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	vvvVarFrameSetInfo	VVVDR2	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefAst	vvvVarFrameSetInfo	VVVDR5	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefAst	vvvVarFrameSetInfo	VVVv20110718	Use a default model for the astrometric noise in Ks band.	tinyint	1		0
ksisDefAst	vvvxVarFrameSetInfo	VVVXDR1	Use a default model for the astrometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	sharksVarFrameSetInfo	SHARKSv20210222	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	sharksVarFrameSetInfo	SHARKSv20210421	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	videoVarFrameSetInfo	VIDEODR2	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	videoVarFrameSetInfo	VIDEODR3	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	videoVarFrameSetInfo	VIDEODR4	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	videoVarFrameSetInfo	VIDEODR5	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	videoVarFrameSetInfo	VIDEOv20111208	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vikingVarFrameSetInfo	VIKINGDR2	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vikingVarFrameSetInfo	VIKINGv20111019	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vmcVarFrameSetInfo	VMCDR1	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vmcVarFrameSetInfo	VMCDR2	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	vmcVarFrameSetInfo	VMCDR3	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCDR4	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCDR5	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20110816	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vmcVarFrameSetInfo	VMCv20110909	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vmcVarFrameSetInfo	VMCv20120126	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vmcVarFrameSetInfo	VMCv20121128	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	vmcVarFrameSetInfo	VMCv20130304	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	vmcVarFrameSetInfo	VMCv20130805	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	vmcVarFrameSetInfo	VMCv20140428	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20140903	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20150309	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20151218	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20160311	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20160822	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20170109	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20170411	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20171101	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20180702	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20181120	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20191212	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20210708	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20230816	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcVarFrameSetInfo	VMCv20240226	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vvvVarFrameSetInfo	VVVDR1	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	vvvVarFrameSetInfo	VVVDR2	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.NIR
ksisDefPht	vvvVarFrameSetInfo	VVVDR5	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksisDefPht	vvvVarFrameSetInfo	VVVv20110718	Use a default model for the photometric noise in Ks band.	tinyint	1		0
ksisDefPht	vvvxVarFrameSetInfo	VVVXDR1	Use a default model for the photometric noise in Ks band.	tinyint	1		0	meta.code;em.IR.K
ksIsMeas	ultravistaSourceRemeasurement	ULTRAVISTADR4	Is pass band Ks measured? 0 no, 1 yes	tinyint	1		0	meta.code
ksIsMeas	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Is pass band Ks measured? 0 no, 1 yes	tinyint	1		0	meta.code
ksIsMeas	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Is pass band Ks measured? 0 no, 1 yes	tinyint	1		0	meta.code
ksKronJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks calibrated flux (Kron)	real	4	jansky	-0.9999995e9	phot.flux
ksKronJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Ks calibrated flux (Kron)	real	4	janksy	-0.9999995e9	stat.error
ksKronLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks luptitude (Kron)	real	4	lup	-0.9999995e9	phot.lup
ksKronLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Ks luptitude (Kron)	real	4	lup	-0.9999995e9	stat.error
ksKronMag	ultravistaSource	ULTRAVISTADR4	Extended source Ks mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksKronMag	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks magnitude (Kron)	real	4	mag	-0.9999995e9	phot.mag
ksKronMag	videoSource	VIDEODR4	Extended source Ks mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksKronMag	videoSource	VIDEODR5	Extended source Ks mag (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksKronMagErr	ultravistaSource	ULTRAVISTADR4	Extended source Ks mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksKronMagErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Ks magnitude (Kron)	real	4	mag	-0.9999995e9	stat.error
ksKronMagErr	videoSource	VIDEODR4	Extended source Ks mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksKronMagErr	videoSource	VIDEODR5	Extended source Ks mag error (Kron - SExtractor MAG_AUTO)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksMag	vhsSourceRemeasurement	VHSDR1	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	videoSourceRemeasurement	VIDEOv20100513	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	vikingSourceRemeasurement	VIKINGv20110714	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	vikingSourceRemeasurement	VIKINGv20111019	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	vmcSourceRemeasurement	VMCv20110816	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	vmcSourceRemeasurement	VMCv20110909	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	vvvSourceRemeasurement	VVVv20100531	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMag	vvvSourceRemeasurement	VVVv20110718	Ks mag (as appropriate for this merged source)	real	4	mag	-0.9999995e9	phot.mag
ksMagErr	vhsSourceRemeasurement	VHSDR1	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	videoSourceRemeasurement	VIDEOv20100513	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	vikingSourceRemeasurement	VIKINGv20110714	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	vikingSourceRemeasurement	VIKINGv20111019	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	vmcCepheidVariables	VMCDR3	Error in mean Ks band magnitude {catalogue TType keyword: err_Ks}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCDR4	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20121128	Error in mean Ks band magnitude {catalogue TType keyword: err_Ks}	real	4	mag	-0.9999995e9	stat.error;em.IR.NIR
ksMagErr	vmcCepheidVariables	VMCv20140428	Error in mean Ks band magnitude {catalogue TType keyword: err_Ks}	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20140903	Error in mean Ks band magnitude {catalogue TType keyword: err_Ks}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20150309	Error in mean Ks band magnitude {catalogue TType keyword: err_Ks}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20151218	Error in mean Ks band magnitude {catalogue TType keyword: err_Ks}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20160311	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20160822	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20170109	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20170411	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20171101	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20180702	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20181120	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20191212	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20210708	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20230816	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcCepheidVariables	VMCv20240226	Error in intensity-averaged Ks band magnitude {catalogue TType keyword: e_Ksmag}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksMagErr	vmcSourceRemeasurement	VMCv20110816	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	vmcSourceRemeasurement	VMCv20110909	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	vvvSourceRemeasurement	VVVv20100531	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagErr	vvvSourceRemeasurement	VVVv20110718	Error in Ks mag	real	4	mag	-0.9999995e9	stat.error
ksMagMAD	sharksVariability	SHARKSv20210222	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	sharksVariability	SHARKSv20210421	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	ultravistaMapLcVariability	ULTRAVISTADR4	Median Absolute Deviation of Ks 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.
ksMagMAD	ultravistaVariability	ULTRAVISTADR4	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	videoVariability	VIDEODR2	Median Absolute Deviation of Ks 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.
ksMagMAD	videoVariability	VIDEODR3	Median Absolute Deviation of Ks 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.
ksMagMAD	videoVariability	VIDEODR4	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	videoVariability	VIDEODR5	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	videoVariability	VIDEOv20100513	Median Absolute Deviation of Ks 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.
ksMagMAD	videoVariability	VIDEOv20111208	Median Absolute Deviation of Ks 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.
ksMagMAD	vikingVariability	VIKINGDR2	Median Absolute Deviation of Ks 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.
ksMagMAD	vikingVariability	VIKINGv20110714	Median Absolute Deviation of Ks 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.
ksMagMAD	vikingVariability	VIKINGv20111019	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCDR1	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCDR2	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCDR3	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCDR4	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCDR5	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20110816	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCv20110909	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCv20120126	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCv20121128	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCv20130304	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCv20130805	Median Absolute Deviation of Ks 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.
ksMagMAD	vmcVariability	VMCv20140428	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagMAD	vmcVariability	VMCv20140903	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20150309	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20151218	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20160311	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20160822	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20170109	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20170411	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20171101	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20180702	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20181120	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20191212	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20210708	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20230816	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcVariability	VMCv20240226	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcdeepVariability	VMCDEEPv20230713	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vmcdeepVariability	VMCDEEPv20240506	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vvvVariability	VVVDR1	Median Absolute Deviation of Ks 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.
ksMagMAD	vvvVariability	VVVDR2	Median Absolute Deviation of Ks 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.
ksMagMAD	vvvVariability	VVVDR5	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagMAD	vvvVariability	VVVv20100531	Median Absolute Deviation of Ks 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.
ksMagMAD	vvvVariability	VVVv20110718	Median Absolute Deviation of Ks 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.
ksMagMAD	vvvxVariability	VVVXDR1	Median Absolute Deviation of Ks magnitude	real	4	mag	-0.9999995e9	stat.err;em.IR.K;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.
ksMagRms	sharksVariability	SHARKSv20210222	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	sharksVariability	SHARKSv20210421	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	ultravistaMapLcVariability	ULTRAVISTADR4	rms of Ks 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.
ksMagRms	ultravistaVariability	ULTRAVISTADR4	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	videoVariability	VIDEODR2	rms of Ks 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.
ksMagRms	videoVariability	VIDEODR3	rms of Ks 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.
ksMagRms	videoVariability	VIDEODR4	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.K;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.
ksMagRms	videoVariability	VIDEODR5	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.K;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.
ksMagRms	videoVariability	VIDEOv20100513	rms of Ks 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.
ksMagRms	videoVariability	VIDEOv20111208	rms of Ks 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.
ksMagRms	vikingVariability	VIKINGDR2	rms of Ks 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.
ksMagRms	vikingVariability	VIKINGv20110714	rms of Ks 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.
ksMagRms	vikingVariability	VIKINGv20111019	rms of Ks 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.
ksMagRms	vmcVariability	VMCDR1	rms of Ks 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.
ksMagRms	vmcVariability	VMCDR2	rms of Ks 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.
ksMagRms	vmcVariability	VMCDR3	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.K;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.
ksMagRms	vmcVariability	VMCDR4	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCDR5	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20110816	rms of Ks 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.
ksMagRms	vmcVariability	VMCv20110909	rms of Ks 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.
ksMagRms	vmcVariability	VMCv20120126	rms of Ks 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.
ksMagRms	vmcVariability	VMCv20121128	rms of Ks 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.
ksMagRms	vmcVariability	VMCv20130304	rms of Ks 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.
ksMagRms	vmcVariability	VMCv20130805	rms of Ks 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.
ksMagRms	vmcVariability	VMCv20140428	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20140903	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.K;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.
ksMagRms	vmcVariability	VMCv20150309	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;em.IR.K;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.
ksMagRms	vmcVariability	VMCv20151218	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20160311	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20160822	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20170109	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20170411	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20171101	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20180702	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20181120	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20191212	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20210708	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20230816	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcVariability	VMCv20240226	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcdeepVariability	VMCDEEPv20230713	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vmcdeepVariability	VMCDEEPv20240506	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vvvVariability	VVVDR1	rms of Ks 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.
ksMagRms	vvvVariability	VVVDR2	rms of Ks 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.
ksMagRms	vvvVariability	VVVDR5	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMagRms	vvvVariability	VVVv20100531	rms of Ks 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.
ksMagRms	vvvVariability	VVVv20110718	rms of Ks 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.
ksMagRms	vvvxVariability	VVVXDR1	rms of Ks magnitude	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksMax	vmcEclipsingBinaryVariables	VMCDR4	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20140903	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20150309	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20151218	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20160311	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20160822	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20170109	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20170411	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20171101	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20180702	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20181120	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20191212	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20210708	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20230816	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksMax	vmcEclipsingBinaryVariables	VMCv20240226	Ks magnitude at maximum light, determined by fitting with GRATIS {catalogue TType keyword: KS_MAX}	real	4	mag		phot.mag;stat.max;em.IR.K
ksmaxCadence	sharksVariability	SHARKSv20210222	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.
ksmaxCadence	sharksVariability	SHARKSv20210421	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.
ksmaxCadence	ultravistaMapLcVariability	ULTRAVISTADR4	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	ultravistaVariability	ULTRAVISTADR4	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	videoVariability	VIDEODR5	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	vikingVariability	VIKINGDR2	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	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.
ksmaxCadence	vikingVariability	VIKINGv20111019	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	vmcVariability	VMCDR4	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCDR5	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	vmcVariability	VMCv20140428	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	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.
ksmaxCadence	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.
ksmaxCadence	vmcVariability	VMCv20151218	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20160311	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20160822	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20170109	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20170411	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20171101	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20180702	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20181120	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20191212	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20210708	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20230816	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcVariability	VMCv20240226	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcdeepVariability	VMCDEEPv20230713	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vmcdeepVariability	VMCDEEPv20240506	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vvvVariability	VVVDR1	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.
ksmaxCadence	vvvVariability	VVVDR2	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.
ksmaxCadence	vvvVariability	VVVDR5	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vvvVariability	VVVv20100531	maximum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmaxCadence	vvvVariability	VVVv20110718	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.
ksmaxCadence	vvvxVariability	VVVXDR1	maximum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.max
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksMaxErr	vmcEclipsingBinaryVariables	VMCDR4	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20140903	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20150309	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20151218	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20160311	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20160822	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20170109	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20170411	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20171101	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20180702	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20181120	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20191212	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20210708	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20230816	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxErr	vmcEclipsingBinaryVariables	VMCv20240226	Error on Ks magnitude at maximum light {catalogue TType keyword: ERROR}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMaxMag	sharksVariability	SHARKSv20210222	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	sharksVariability	SHARKSv20210421	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	ultravistaMapLcVariability	ULTRAVISTADR4	Maximum magnitude in Ks 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.
ksMaxMag	ultravistaVariability	ULTRAVISTADR4	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	videoVariability	VIDEODR2	Maximum magnitude in Ks 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.
ksMaxMag	videoVariability	VIDEODR3	Maximum magnitude in Ks 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.
ksMaxMag	videoVariability	VIDEODR4	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	videoVariability	VIDEODR5	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	videoVariability	VIDEOv20100513	Maximum magnitude in Ks 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.
ksMaxMag	videoVariability	VIDEOv20111208	Maximum magnitude in Ks 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.
ksMaxMag	vikingVariability	VIKINGDR2	Maximum magnitude in Ks 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.
ksMaxMag	vikingVariability	VIKINGv20110714	Maximum magnitude in Ks 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.
ksMaxMag	vikingVariability	VIKINGv20111019	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCDR1	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCDR2	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCDR3	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCDR4	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCDR5	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20110816	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCv20110909	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCv20120126	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCv20121128	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCv20130304	Maximum magnitude in Ks 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.
ksMaxMag	vmcVariability	VMCv20130805	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20140428	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20140903	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20150309	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20151218	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20160311	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20160822	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20170109	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20170411	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20171101	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20180702	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20181120	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20191212	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20210708	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20230816	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcVariability	VMCv20240226	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcdeepVariability	VMCDEEPv20230713	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vmcdeepVariability	VMCDEEPv20240506	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vvvVariability	VVVDR1	Maximum magnitude in Ks 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.
ksMaxMag	vvvVariability	VVVDR2	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vvvVariability	VVVDR5	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMaxMag	vvvVariability	VVVv20100531	Maximum magnitude in Ks 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.
ksMaxMag	vvvVariability	VVVv20110718	Maximum magnitude in Ks 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.
ksMaxMag	vvvxVariability	VVVXDR1	Maximum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMean	vmcRRLyraeVariables	VMCv20240226	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_MEAN}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCDR4	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20160822	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20170109	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20170411	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20171101	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20180702	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20181120	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20191212	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20210708	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMean	vmcRRlyraeVariables	VMCv20230816	Mean K_s band magnitude derived with GRATIS {catalogue TType keyword: KS_GRATIS}	real	4	mag		phot.mag;stat.mean;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCDR4	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20160822	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20170109	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20170411	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20171101	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20180702	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20181120	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20191212	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20210708	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanErr	vmcRRlyraeVariables	VMCv20230816	Uncertainty in mean Ksmag provided by GRATIS {catalogue TType keyword: ERR_KGRATIS}	real	4	mag		stat.error;phot.mag;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCDR3	Mean Ks band magnitude {catalogue TType keyword: Ks}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCDR4	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20121128	Mean Ks band magnitude {catalogue TType keyword: Ks}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.NIR
ksMeanMag	vmcCepheidVariables	VMCv20140428	Mean Ks band magnitude {catalogue TType keyword: Ks}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20140903	Mean Ks band magnitude {catalogue TType keyword: Ks}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20150309	Mean Ks band magnitude {catalogue TType keyword: Ks}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20151218	Mean Ks band magnitude {catalogue TType keyword: Ks}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20160311	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20160822	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20170109	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20170411	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20171101	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20180702	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20181120	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20191212	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20210708	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20230816	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksMeanMag	vmcCepheidVariables	VMCv20240226	Intensity-averaged Ks band magnitude {catalogue TType keyword: Ksmag}	real	4	mag	-0.9999995e9	phot.mag;stat.mean;em.IR.K
ksmeanMag	sharksVariability	SHARKSv20210222	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	sharksVariability	SHARKSv20210421	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	ultravistaMapLcVariability	ULTRAVISTADR4	Mean Ks 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.
ksmeanMag	ultravistaVariability	ULTRAVISTADR4	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	videoVariability	VIDEODR2	Mean Ks 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.
ksmeanMag	videoVariability	VIDEODR3	Mean Ks 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.
ksmeanMag	videoVariability	VIDEODR4	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	videoVariability	VIDEODR5	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	videoVariability	VIDEOv20100513	Mean Ks 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.
ksmeanMag	videoVariability	VIDEOv20111208	Mean Ks 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.
ksmeanMag	vikingVariability	VIKINGDR2	Mean Ks 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.
ksmeanMag	vikingVariability	VIKINGv20110714	Mean Ks 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.
ksmeanMag	vikingVariability	VIKINGv20111019	Mean Ks 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.
ksmeanMag	vmcVariability	VMCDR1	Mean Ks 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.
ksmeanMag	vmcVariability	VMCDR2	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksmeanMag	vmcVariability	VMCDR3	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCDR4	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCDR5	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20110816	Mean Ks 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.
ksmeanMag	vmcVariability	VMCv20110909	Mean Ks 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.
ksmeanMag	vmcVariability	VMCv20120126	Mean Ks 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.
ksmeanMag	vmcVariability	VMCv20121128	Mean Ks 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.
ksmeanMag	vmcVariability	VMCv20130304	Mean Ks 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.
ksmeanMag	vmcVariability	VMCv20130805	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksmeanMag	vmcVariability	VMCv20140428	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20140903	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20150309	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20151218	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20160311	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20160822	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20170109	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20170411	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20171101	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20180702	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20181120	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20191212	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20210708	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20230816	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcVariability	VMCv20240226	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcdeepVariability	VMCDEEPv20230713	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vmcdeepVariability	VMCDEEPv20240506	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vvvVariability	VVVDR1	Mean Ks 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.
ksmeanMag	vvvVariability	VVVDR2	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksmeanMag	vvvVariability	VVVDR5	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmeanMag	vvvVariability	VVVv20100531	Mean Ks 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.
ksmeanMag	vvvVariability	VVVv20110718	Mean Ks 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.
ksmeanMag	vvvxVariability	VVVXDR1	Mean Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.mean;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedCadence	sharksVariability	SHARKSv20210222	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.
ksmedCadence	sharksVariability	SHARKSv20210421	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.
ksmedCadence	ultravistaMapLcVariability	ULTRAVISTADR4	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	ultravistaVariability	ULTRAVISTADR4	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	videoVariability	VIDEODR5	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	vikingVariability	VIKINGDR2	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	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.
ksmedCadence	vikingVariability	VIKINGv20111019	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	vmcVariability	VMCDR4	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCDR5	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	vmcVariability	VMCv20140428	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	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.
ksmedCadence	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.
ksmedCadence	vmcVariability	VMCv20151218	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20160311	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20160822	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20170109	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20170411	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20171101	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20180702	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20181120	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20191212	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20210708	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20230816	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcVariability	VMCv20240226	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcdeepVariability	VMCDEEPv20230713	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vmcdeepVariability	VMCDEEPv20240506	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vvvVariability	VVVDR1	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.
ksmedCadence	vvvVariability	VVVDR2	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.
ksmedCadence	vvvVariability	VVVDR5	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vvvVariability	VVVv20100531	median gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedCadence	vvvVariability	VVVv20110718	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.
ksmedCadence	vvvxVariability	VVVXDR1	median gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.median
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksmedianMag	sharksVariability	SHARKSv20210222	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	sharksVariability	SHARKSv20210421	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	ultravistaMapLcVariability	ULTRAVISTADR4	Median Ks 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.
ksmedianMag	ultravistaVariability	ULTRAVISTADR4	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	videoVariability	VIDEODR2	Median Ks 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.
ksmedianMag	videoVariability	VIDEODR3	Median Ks 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.
ksmedianMag	videoVariability	VIDEODR4	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	videoVariability	VIDEODR5	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	videoVariability	VIDEOv20100513	Median Ks 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.
ksmedianMag	videoVariability	VIDEOv20111208	Median Ks 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.
ksmedianMag	vikingVariability	VIKINGDR2	Median Ks 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.
ksmedianMag	vikingVariability	VIKINGv20110714	Median Ks 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.
ksmedianMag	vikingVariability	VIKINGv20111019	Median Ks 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.
ksmedianMag	vmcVariability	VMCDR1	Median Ks 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.
ksmedianMag	vmcVariability	VMCDR2	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksmedianMag	vmcVariability	VMCDR3	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCDR4	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCDR5	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20110816	Median Ks 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.
ksmedianMag	vmcVariability	VMCv20110909	Median Ks 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.
ksmedianMag	vmcVariability	VMCv20120126	Median Ks 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.
ksmedianMag	vmcVariability	VMCv20121128	Median Ks 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.
ksmedianMag	vmcVariability	VMCv20130304	Median Ks 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.
ksmedianMag	vmcVariability	VMCv20130805	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksmedianMag	vmcVariability	VMCv20140428	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20140903	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20150309	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20151218	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20160311	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20160822	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20170109	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20170411	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20171101	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20180702	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20181120	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20191212	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20210708	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20230816	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcVariability	VMCv20240226	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcdeepVariability	VMCDEEPv20230713	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vmcdeepVariability	VMCDEEPv20240506	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vvvVariability	VVVDR1	Median Ks 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.
ksmedianMag	vvvVariability	VVVDR2	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksmedianMag	vvvVariability	VVVDR5	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmedianMag	vvvVariability	VVVv20100531	Median Ks 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.
ksmedianMag	vvvVariability	VVVv20110718	Median Ks 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.
ksmedianMag	vvvxVariability	VVVXDR1	Median Ks magnitude	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;stat.median;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksmfID	sharksMergeLog	SHARKSv20210222	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	sharksMergeLog	SHARKSv20210421	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	ultravistaMergeLog, ultravistaRemeasMergeLog	ULTRAVISTADR4	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSDR1	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vhsMergeLog	VHSDR2	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vhsMergeLog	VHSDR3	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSDR4	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSDR5	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSDR6	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20120926	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vhsMergeLog	VHSv20130417	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vhsMergeLog	VHSv20140409	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20150108	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20160114	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20160507	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20170630	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20180419	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20201209	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20231101	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vhsMergeLog	VHSv20240731	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	videoMergeLog	VIDEODR2	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	videoMergeLog	VIDEODR3	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	videoMergeLog	VIDEODR4	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	videoMergeLog	VIDEODR5	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	videoMergeLog	VIDEOv20100513	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	videoMergeLog	VIDEOv20111208	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vikingMergeLog	VIKINGDR2	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vikingMergeLog	VIKINGDR3	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vikingMergeLog	VIKINGDR4	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vikingMergeLog	VIKINGv20110714	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vikingMergeLog	VIKINGv20111019	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vikingMergeLog	VIKINGv20130417	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vikingMergeLog	VIKINGv20140402	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vikingMergeLog	VIKINGv20150421	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vikingMergeLog	VIKINGv20151230	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vikingMergeLog	VIKINGv20160406	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vikingMergeLog	VIKINGv20161202	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vikingMergeLog	VIKINGv20170715	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20170124	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vmcMergeLog	VMCDR2	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vmcMergeLog	VMCDR3	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCDR4	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCDR5	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20110816	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vmcMergeLog	VMCv20110909	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vmcMergeLog	VMCv20120126	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vmcMergeLog	VMCv20121128	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vmcMergeLog	VMCv20130304	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vmcMergeLog	VMCv20130805	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vmcMergeLog	VMCv20140428	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20140903	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20150309	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20151218	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20160311	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20160822	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20170109	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20170411	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20171101	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20180702	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20181120	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20191212	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20210708	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20230816	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog	VMCv20240226	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcMergeLog, vmcSynopticMergeLog	VMCDR1	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vmcdeepMergeLog	VMCDEEPv20240506	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vmcdeepMergeLog, vmcdeepSynopticMergeLog	VMCDEEPv20230713	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vvvMergeLog	VVVDR2	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vvvMergeLog	VVVv20100531	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vvvMergeLog	VVVv20110718	the UID of the relevant Ks multiframe	bigint	8			obs.field
ksmfID	vvvMergeLog, vvvPsfDaophotJKsMergeLog	VVVDR5	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksmfID	vvvMergeLog, vvvSynopticMergeLog	VVVDR1	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field
ksmfID	vvvxMergeLog	VVVXDR1	the UID of the relevant Ks multiframe	bigint	8			meta.id;obs.field;em.IR.K
ksminCadence	sharksVariability	SHARKSv20210222	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.
ksminCadence	sharksVariability	SHARKSv20210421	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.
ksminCadence	ultravistaMapLcVariability	ULTRAVISTADR4	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	ultravistaVariability	ULTRAVISTADR4	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	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.
ksminCadence	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.
ksminCadence	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.
ksminCadence	videoVariability	VIDEODR5	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	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.
ksminCadence	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.
ksminCadence	vikingVariability	VIKINGDR2	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	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.
ksminCadence	vikingVariability	VIKINGv20111019	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	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.
ksminCadence	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.
ksminCadence	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.
ksminCadence	vmcVariability	VMCDR4	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCDR5	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	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.
ksminCadence	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.
ksminCadence	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.
ksminCadence	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.
ksminCadence	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.
ksminCadence	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.
ksminCadence	vmcVariability	VMCv20140428	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	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.
ksminCadence	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.
ksminCadence	vmcVariability	VMCv20151218	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20160311	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20160822	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20170109	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20170411	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20171101	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20180702	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20181120	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20191212	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20210708	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20230816	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcVariability	VMCv20240226	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcdeepVariability	VMCDEEPv20230713	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vmcdeepVariability	VMCDEEPv20240506	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vvvVariability	VVVDR1	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.
ksminCadence	vvvVariability	VVVDR2	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.
ksminCadence	vvvVariability	VVVDR5	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vvvVariability	VVVv20100531	minimum gap between observations	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksminCadence	vvvVariability	VVVv20110718	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.
ksminCadence	vvvxVariability	VVVXDR1	minimum gap between observations	real	4	days	-0.9999995e9	time.interval;obs;stat.min
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksMinMag	sharksVariability	SHARKSv20210222	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	sharksVariability	SHARKSv20210421	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	ultravistaMapLcVariability	ULTRAVISTADR4	Minimum magnitude in Ks 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.
ksMinMag	ultravistaVariability	ULTRAVISTADR4	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	videoVariability	VIDEODR2	Minimum magnitude in Ks 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.
ksMinMag	videoVariability	VIDEODR3	Minimum magnitude in Ks 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.
ksMinMag	videoVariability	VIDEODR4	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	videoVariability	VIDEODR5	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	videoVariability	VIDEOv20100513	Minimum magnitude in Ks 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.
ksMinMag	videoVariability	VIDEOv20111208	Minimum magnitude in Ks 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.
ksMinMag	vikingVariability	VIKINGDR2	Minimum magnitude in Ks 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.
ksMinMag	vikingVariability	VIKINGv20110714	Minimum magnitude in Ks 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.
ksMinMag	vikingVariability	VIKINGv20111019	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCDR1	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCDR2	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCDR3	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCDR4	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCDR5	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20110816	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCv20110909	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCv20120126	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCv20121128	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCv20130304	Minimum magnitude in Ks 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.
ksMinMag	vmcVariability	VMCv20130805	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20140428	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20140903	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20150309	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20151218	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20160311	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20160822	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20170109	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20170411	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20171101	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20180702	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20181120	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20191212	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20210708	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20230816	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcVariability	VMCv20240226	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcdeepVariability	VMCDEEPv20230713	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vmcdeepVariability	VMCDEEPv20240506	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vvvVariability	VVVDR1	Minimum magnitude in Ks 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.
ksMinMag	vvvVariability	VVVDR2	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vvvVariability	VVVDR5	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMinMag	vvvVariability	VVVv20100531	Minimum magnitude in Ks 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.
ksMinMag	vvvVariability	VVVv20110718	Minimum magnitude in Ks 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.
ksMinMag	vvvxVariability	VVVXDR1	Minimum magnitude in Ks band, of good detections	real	4	mag	-0.9999995e9	phot.mag;em.IR.K;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.
ksMjd	sharksSource	SHARKSv20210222	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	sharksSource	SHARKSv20210421	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	ultravistaSource	ULTRAVISTADR4	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	ultravistaSourceRemeasurement	ULTRAVISTADR4	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vhsSource	VHSDR5	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vhsSource	VHSDR6	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vhsSource	VHSv20160114	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vhsSource	VHSv20160507	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vhsSource	VHSv20170630	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vhsSource	VHSv20180419	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vhsSource	VHSv20201209	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vhsSource	VHSv20231101	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vhsSource	VHSv20240731	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vikingSource	VIKINGv20151230	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vikingSource	VIKINGv20160406	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vikingSource	VIKINGv20161202	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vikingSource	VIKINGv20170715	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcSource	VMCDR5	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20151218	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcSource	VMCv20160311	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcSource	VMCv20160822	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcSource	VMCv20170109	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcSource	VMCv20170411	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcSource	VMCv20171101	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20180702	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20181120	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20191212	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20210708	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20230816	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource	VMCv20240226	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcSource, vmcSynopticSource	VMCDR4	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vmcdeepSource	VMCDEEPv20230713	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcdeepSource	VMCDEEPv20240506	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vmcdeepSynopticSource	VMCDEEPv20230713	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;meta.main
ksMjd	vmcdeepSynopticSource	VMCDEEPv20240506	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;meta.main
ksMjd	vvvSource	VVVDR5	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksMjd	vvvSynopticSource	VVVDR1	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vvvSynopticSource	VVVDR2	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch
ksMjd	vvvxSource	VVVXDR1	Modified Julian Day in Ks band	float	8	days	-0.9999995e9	time.epoch;em.IR.K
ksndof	sharksVariability	SHARKSv20210222	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	sharksVariability	SHARKSv20210421	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	ultravistaMapLcVariability	ULTRAVISTADR4	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	ultravistaVariability	ULTRAVISTADR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	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.
ksndof	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.
ksndof	videoVariability	VIDEODR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	videoVariability	VIDEODR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	videoVariability	VIDEOv20100513	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	videoVariability	VIDEOv20111208	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vikingVariability	VIKINGDR2	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vikingVariability	VIKINGv20110714	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vikingVariability	VIKINGv20111019	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCDR1	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCDR2	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCDR3	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCDR4	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCDR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20110816	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20110909	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20120126	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20121128	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20130304	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20130805	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20140428	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20140903	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20150309	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20151218	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20160311	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20160822	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20170109	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20170411	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20171101	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20180702	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20181120	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20191212	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20210708	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20230816	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcVariability	VMCv20240226	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcdeepVariability	VMCDEEPv20230713	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vmcdeepVariability	VMCDEEPv20240506	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vvvVariability	VVVDR1	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.
ksndof	vvvVariability	VVVDR2	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.
ksndof	vvvVariability	VVVDR5	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vvvVariability	VVVv20100531	Number of degrees of freedom for chisquare	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksndof	vvvVariability	VVVv20110718	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.
ksndof	vvvxVariability	VVVXDR1	Number of degrees of freedom for chisquare	smallint	2		-9999	stat.fit.dof;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksnDofAst	sharksVarFrameSetInfo	SHARKSv20210222	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	sharksVarFrameSetInfo	SHARKSv20210421	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	ultravistaVarFrameSetInfo	ULTRAVISTADR4	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	videoVarFrameSetInfo	VIDEODR2	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	videoVarFrameSetInfo	VIDEODR3	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	videoVarFrameSetInfo	VIDEODR4	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	videoVarFrameSetInfo	VIDEODR5	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	videoVarFrameSetInfo	VIDEOv20100513	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	videoVarFrameSetInfo	VIDEOv20111208	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vikingVarFrameSetInfo	VIKINGDR2	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vikingVarFrameSetInfo	VIKINGv20110714	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vikingVarFrameSetInfo	VIKINGv20111019	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCDR1	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCDR2	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCDR3	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCDR4	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCDR5	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20110816	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20110909	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20120126	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20121128	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20130304	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20130805	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20140428	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20140903	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20150309	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20151218	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20160311	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20160822	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20170109	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20170411	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20171101	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20180702	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20181120	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20191212	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20210708	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20230816	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcVarFrameSetInfo	VMCv20240226	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vvvVarFrameSetInfo	VVVDR1	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vvvVarFrameSetInfo	VVVDR2	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vvvVarFrameSetInfo	VVVDR5	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vvvVarFrameSetInfo	VVVv20100531	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vvvVarFrameSetInfo	VVVv20110718	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofAst	vvvxVarFrameSetInfo	VVVXDR1	Number of degrees of freedom of astrometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
ksnDofPht	sharksVarFrameSetInfo	SHARKSv20210222	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	sharksVarFrameSetInfo	SHARKSv20210421	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo	ULTRAVISTADR4	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	videoVarFrameSetInfo	VIDEODR2	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	videoVarFrameSetInfo	VIDEODR3	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	videoVarFrameSetInfo	VIDEODR4	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	videoVarFrameSetInfo	VIDEODR5	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	videoVarFrameSetInfo	VIDEOv20100513	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	videoVarFrameSetInfo	VIDEOv20111208	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vikingVarFrameSetInfo	VIKINGDR2	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vikingVarFrameSetInfo	VIKINGv20110714	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vikingVarFrameSetInfo	VIKINGv20111019	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCDR1	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCDR2	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCDR3	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCDR4	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCDR5	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20110816	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20110909	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20120126	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20121128	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20130304	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20130805	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20140428	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20140903	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20150309	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20151218	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20160311	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20160822	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20170109	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20170411	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20171101	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20180702	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20181120	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20191212	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20210708	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20230816	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcVarFrameSetInfo	VMCv20240226	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vmcdeepVarFrameSetInfo	VMCDEEPv20240506	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vvvVarFrameSetInfo	VVVDR1	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vvvVarFrameSetInfo	VVVDR2	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.NIR
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vvvVarFrameSetInfo	VVVDR5	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vvvVarFrameSetInfo	VVVv20100531	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vvvVarFrameSetInfo	VVVv20110718	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksnDofPht	vvvxVarFrameSetInfo	VVVXDR1	Number of degrees of freedom of photometric fit in Ks band.	smallint	2		-9999	stat.fit.dof;stat.param;em.IR.K
			The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
ksNepochs	vmcCepheidVariables	VMCDR4	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20160311	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20160822	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20170109	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20170411	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20171101	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20180702	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20181120	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20191212	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20210708	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20230816	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksNepochs	vmcCepheidVariables	VMCv20240226	Number of Ks mag epochs {catalogue TType keyword: o_Ksmag}	int	4		-99999999	meta.number;em.IR.K
ksnFlaggedObs	sharksVariability	SHARKSv20210222	Number of detections in Ks band flagged as potentially spurious by sharksDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	sharksVariability	SHARKSv20210421	Number of detections in Ks band flagged as potentially spurious by sharksDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	ultravistaVariability	ULTRAVISTADR4	Number of detections in Ks band flagged as potentially spurious by ultravistaDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	videoVariability	VIDEODR2	Number of detections in Ks band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	videoVariability	VIDEODR3	Number of detections in Ks band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	videoVariability	VIDEODR4	Number of detections in Ks band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	videoVariability	VIDEODR5	Number of detections in Ks band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	videoVariability	VIDEOv20100513	Number of detections in Ks band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	videoVariability	VIDEOv20111208	Number of detections in Ks band flagged as potentially spurious by videoDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vikingVariability	VIKINGDR2	Number of detections in Ks band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vikingVariability	VIKINGv20110714	Number of detections in Ks band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vikingVariability	VIKINGv20111019	Number of detections in Ks band flagged as potentially spurious by vikingDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCDR1	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCDR2	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCDR3	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCDR4	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCDR5	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20110816	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20110909	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20120126	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20121128	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20130304	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20130805	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20140428	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20140903	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20150309	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20151218	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20160311	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20160822	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20170109	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20170411	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20171101	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20180702	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20181120	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20191212	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20210708	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20230816	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcVariability	VMCv20240226	Number of detections in Ks band flagged as potentially spurious by vmcDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcdeepVariability	VMCDEEPv20230713	Number of detections in Ks band flagged as potentially spurious by vmcdeepDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vmcdeepVariability	VMCDEEPv20240506	Number of detections in Ks band flagged as potentially spurious by vmcdeepDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vvvVariability	VVVDR1	Number of detections in Ks band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vvvVariability	VVVDR2	Number of detections in Ks band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0	meta.number
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vvvVariability	VVVDR5	Number of detections in Ks band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vvvVariability	VVVv20100531	Number of detections in Ks band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vvvVariability	VVVv20110718	Number of detections in Ks band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnFlaggedObs	vvvxVariability	VVVXDR1	Number of detections in Ks band flagged as potentially spurious by vvvDetection.ppErrBits	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	sharksVariability	SHARKSv20210222	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	sharksVariability	SHARKSv20210421	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	ultravistaVariability	ULTRAVISTADR4	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	videoVariability	VIDEODR2	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	videoVariability	VIDEODR3	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	videoVariability	VIDEODR4	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	videoVariability	VIDEODR5	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	videoVariability	VIDEOv20100513	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	videoVariability	VIDEOv20111208	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vikingVariability	VIKINGDR2	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vikingVariability	VIKINGv20110714	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vikingVariability	VIKINGv20111019	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCDR1	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCDR2	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCDR3	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCDR4	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCDR5	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20110816	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20110909	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20120126	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20121128	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20130304	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20130805	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20140428	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20140903	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20150309	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20151218	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20160311	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20160822	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20170109	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20170411	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20171101	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20180702	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20181120	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20191212	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20210708	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20230816	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcVariability	VMCv20240226	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcdeepVariability	VMCDEEPv20230713	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vmcdeepVariability	VMCDEEPv20240506	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vvvVariability	VVVDR1	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vvvVariability	VVVDR2	Number of good detections in Ks band	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vvvVariability	VVVDR5	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vvvVariability	VVVv20100531	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vvvVariability	VVVv20110718	Number of good detections in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnGoodObs	vvvxVariability	VVVXDR1	Number of good detections in Ks band	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksNgt3sig	sharksVariability	SHARKSv20210222	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	sharksVariability	SHARKSv20210421	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	ultravistaMapLcVariability	ULTRAVISTADR4	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	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.
ksNgt3sig	ultravistaVariability	ULTRAVISTADR4	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	videoVariability	VIDEODR2	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	videoVariability	VIDEODR3	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	videoVariability	VIDEODR4	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	videoVariability	VIDEODR5	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	videoVariability	VIDEOv20100513	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	videoVariability	VIDEOv20111208	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vikingVariability	VIKINGDR2	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vikingVariability	VIKINGv20110714	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vikingVariability	VIKINGv20111019	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCDR1	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCDR2	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCDR3	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCDR4	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCDR5	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20110816	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20110909	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20120126	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20121128	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20130304	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20130805	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20140428	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20140903	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20150309	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20151218	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20160311	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20160822	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20170109	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20170411	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20171101	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20180702	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20181120	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20191212	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20210708	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20230816	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcVariability	VMCv20240226	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcdeepVariability	VMCDEEPv20230713	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vmcdeepVariability	VMCDEEPv20240506	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vvvVariability	VVVDR1	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vvvVariability	VVVDR2	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999	meta.number;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vvvVariability	VVVDR5	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vvvVariability	VVVv20100531	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vvvVariability	VVVv20110718	Number of good detections in Ks-band that are more than 3 sigma deviations	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksNgt3sig	vvvxVariability	VVVXDR1	Number of good detections in Ks-band that are more than 3 sigma deviations (ksAperMagN < (ksMeanMag-3*ksMagRms)	smallint	2		-9999	meta.number;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksnMissingObs	sharksVariability	SHARKSv20210222	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	sharksVariability	SHARKSv20210421	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	ultravistaVariability	ULTRAVISTADR4	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	videoVariability	VIDEODR2	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	videoVariability	VIDEODR3	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	videoVariability	VIDEODR4	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	videoVariability	VIDEODR5	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	videoVariability	VIDEOv20100513	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	videoVariability	VIDEOv20111208	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vikingVariability	VIKINGDR2	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vikingVariability	VIKINGv20110714	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vikingVariability	VIKINGv20111019	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCDR1	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCDR2	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCDR3	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCDR4	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCDR5	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20110816	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20110909	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20120126	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20121128	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20130304	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20130805	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20140428	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20140903	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20150309	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20151218	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20160311	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20160822	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20170109	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20170411	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20171101	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20180702	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20181120	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20191212	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20210708	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20230816	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcVariability	VMCv20240226	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcdeepVariability	VMCDEEPv20230713	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vmcdeepVariability	VMCDEEPv20240506	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vvvVariability	VVVDR1	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vvvVariability	VVVDR2	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.NIR
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vvvVariability	VVVDR5	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vvvVariability	VVVv20100531	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vvvVariability	VVVv20110718	Number of Ks band frames that this object should have been detected on and was not	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnMissingObs	vvvxVariability	VVVXDR1	Number of Ks band frames that this object should have been detected on and was not	int	4		0	meta.number;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnNegFlagObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of flagged negative measurements in Ks band by ultravistaMapRemeasurement.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnNegObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of unflagged negative measurements Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnPosFlagObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of flagged positive measurements in Ks band by ultravistaMapRemeasurement.ppErrBits	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksnPosObs	ultravistaMapLcVariability	ULTRAVISTADR4	Number of unflagged positive measurements in Ks band	int	4		0
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksPA	sharksSource	SHARKSv20210222	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	sharksSource	SHARKSv20210421	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	ultravistaSource	ULTRAVISTADR4	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	ultravistaSourceRemeasurement	ULTRAVISTADR4	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vhsSource	VHSDR2	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vhsSource	VHSDR3	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSDR4	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSDR5	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSDR6	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20120926	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vhsSource	VHSv20130417	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vhsSource	VHSv20140409	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20150108	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20160114	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20160507	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20170630	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20180419	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20201209	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20231101	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource	VHSv20240731	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vhsSource, vhsSourceRemeasurement	VHSDR1	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	videoSource	VIDEODR2	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	videoSource	VIDEODR3	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	videoSource	VIDEODR4	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	videoSource	VIDEODR5	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	videoSource	VIDEOv20111208	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	videoSource, videoSourceRemeasurement	VIDEOv20100513	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingSource	VIKINGDR2	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingSource	VIKINGDR3	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingSource	VIKINGDR4	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vikingSource	VIKINGv20111019	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingSource	VIKINGv20130417	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingSource	VIKINGv20140402	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingSource	VIKINGv20150421	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vikingSource	VIKINGv20151230	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vikingSource	VIKINGv20160406	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vikingSource	VIKINGv20161202	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vikingSource	VIKINGv20170715	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vikingSource, vikingSourceRemeasurement	VIKINGv20110714	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCDR2	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCDR3	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCDR4	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCDR5	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20110909	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCv20120126	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCv20121128	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCv20130304	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCv20130805	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource	VMCv20140428	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20140903	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20150309	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20151218	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20160311	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20160822	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20170109	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20170411	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20171101	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20180702	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20181120	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20191212	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20210708	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20230816	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource	VMCv20240226	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcSource, vmcSourceRemeasurement	VMCv20110816	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcSource, vmcSynopticSource	VMCDR1	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vmcdeepSource	VMCDEEPv20240506	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vvvSource	VVVDR2	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vvvSource	VVVDR5	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPA	vvvSource	VVVv20110718	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vvvSource, vvvSourceRemeasurement	VVVv20100531	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vvvSource, vvvSynopticSource	VVVDR1	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng
ksPA	vvvxSource	VVVXDR1	ellipse fit celestial orientation in Ks	real	4	Degrees	-0.9999995e9	pos.posAng;em.IR.K
ksPawprintMeanMag	vvvVivaCatalogue	VVVDR5	K_s mean magnitude using pawprint data (2.0 arcsec aperture diameter) {catalogue TType keyword: ksEMeanMagPawprint}	real	4	mag	-9.999995e8
ksPetroJky	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks calibrated flux (Petrosian)	real	4	jansky	-0.9999995e9	phot.flux
ksPetroJkyErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Ks calibrated flux (Petrosian)	real	4	jansky	-0.9999995e9	stat.error
ksPetroLup	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks luptitude (Petrosian)	real	4	lup	-0.9999995e9	phot.lup
ksPetroLupErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Ks luptitude (Petrosian)	real	4	lup	-0.9999995e9	stat.error
ksPetroMag	sharksSource	SHARKSv20210222	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	sharksSource	SHARKSv20210421	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	ultravistaSource	ULTRAVISTADR4	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	ultravistaSourceRemeasurement	ULTRAVISTADR4	Extended source Ks magnitude (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vhsSource	VHSDR1	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vhsSource	VHSDR2	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vhsSource	VHSDR3	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSDR4	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSDR5	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSDR6	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20120926	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vhsSource	VHSv20130417	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vhsSource	VHSv20140409	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20150108	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20160114	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20160507	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20170630	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20180419	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20201209	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20231101	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vhsSource	VHSv20240731	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	videoSource	VIDEODR2	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	videoSource	VIDEODR3	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	videoSource	VIDEODR4	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	videoSource	VIDEODR5	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	videoSource	VIDEOv20100513	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	videoSource	VIDEOv20111208	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vikingSource	VIKINGDR2	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vikingSource	VIKINGDR3	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vikingSource	VIKINGDR4	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vikingSource	VIKINGv20110714	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vikingSource	VIKINGv20111019	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vikingSource	VIKINGv20130417	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vikingSource	VIKINGv20140402	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vikingSource	VIKINGv20150421	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vikingSource	VIKINGv20151230	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vikingSource	VIKINGv20160406	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vikingSource	VIKINGv20161202	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vikingSource	VIKINGv20170715	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCDR1	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vmcSource	VMCDR2	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCDR3	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCDR4	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCDR5	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20110816	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vmcSource	VMCv20110909	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vmcSource	VMCv20120126	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vmcSource	VMCv20121128	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vmcSource	VMCv20130304	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag
ksPetroMag	vmcSource	VMCv20130805	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20140428	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20140903	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20150309	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20151218	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20160311	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20160822	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20170109	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20170411	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20171101	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20180702	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20181120	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20191212	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20210708	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20230816	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcSource	VMCv20240226	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcdeepSource	VMCDEEPv20230713	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMag	vmcdeepSource	VMCDEEPv20240506	Extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPetroMagErr	sharksSource	SHARKSv20210222	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	sharksSource	SHARKSv20210421	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	ultravistaSource	ULTRAVISTADR4	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	ultravistaSourceRemeasurement	ULTRAVISTADR4	Error in extended source Ks magnitude (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vhsSource	VHSDR1	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vhsSource	VHSDR2	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vhsSource	VHSDR3	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPetroMagErr	vhsSource	VHSDR4	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	vhsSource	VHSDR5	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSDR6	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20120926	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vhsSource	VHSv20130417	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vhsSource	VHSv20140409	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPetroMagErr	vhsSource	VHSv20150108	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	vhsSource	VHSv20160114	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20160507	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20170630	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20180419	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20201209	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20231101	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vhsSource	VHSv20240731	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	videoSource	VIDEODR2	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	videoSource	VIDEODR3	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	videoSource	VIDEODR4	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	videoSource	VIDEODR5	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	videoSource	VIDEOv20100513	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	videoSource	VIDEOv20111208	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGDR2	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGDR3	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGDR4	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPetroMagErr	vikingSource	VIKINGv20110714	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGv20111019	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGv20130417	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGv20140402	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vikingSource	VIKINGv20150421	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	vikingSource	VIKINGv20151230	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vikingSource	VIKINGv20160406	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vikingSource	VIKINGv20161202	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vikingSource	VIKINGv20170715	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCDR1	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCDR2	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCDR3	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	vmcSource	VMCDR4	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCDR5	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20110816	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCv20110909	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCv20120126	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCv20121128	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCv20130304	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCv20130805	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error
ksPetroMagErr	vmcSource	VMCv20140428	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPetroMagErr	vmcSource	VMCv20140903	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	vmcSource	VMCv20150309	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPetroMagErr	vmcSource	VMCv20151218	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20160311	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20160822	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20170109	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20170411	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20171101	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20180702	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20181120	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20191212	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20210708	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20230816	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcSource	VMCv20240226	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcdeepSource	VMCDEEPv20230713	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPetroMagErr	vmcdeepSource	VMCDEEPv20240506	Error in extended source Ks mag (Petrosian)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksppErrBits	sharksSource	SHARKSv20210222	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	sharksSource	SHARKSv20210421	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	ultravistaSource	ULTRAVISTADR4	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
ksppErrBits	ultravistaSourceRemeasurement	ULTRAVISTADR4	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSDR1	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSDR2	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSDR3	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSDR4	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSDR5	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSDR6	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20120926	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20130417	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20140409	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20150108	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20160114	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20160507	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20170630	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20180419	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20201209	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20231101	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSource	VHSv20240731	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vhsSourceRemeasurement	VHSDR1	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	videoSource	VIDEODR2	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	videoSource	VIDEODR3	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	videoSource	VIDEODR4	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
ksppErrBits	videoSource	VIDEODR5	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
ksppErrBits	videoSource	VIDEOv20111208	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	videoSource, videoSourceRemeasurement	VIDEOv20100513	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vikingSource	VIKINGDR2	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGDR3	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGDR4	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20110714	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20111019	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20130417	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20140402	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20150421	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20151230	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20160406	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20161202	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSource	VIKINGv20170715	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingSourceRemeasurement	VIKINGv20110714	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vikingSourceRemeasurement	VIKINGv20111019	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20160909	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vikingZY_selJ_SourceRemeasurement	VIKINGZYSELJv20170124	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCDR2	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCDR3	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCDR4	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCDR5	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20110816	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20110909	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20120126	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20121128	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20130304	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20130805	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20140428	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20140903	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20150309	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20151218	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20160311	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20160822	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20170109	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20170411	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20171101	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20180702	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20181120	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20191212	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20210708	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20230816	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource	VMCv20240226	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSource, vmcSynopticSource	VMCDR1	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcSourceRemeasurement	VMCv20110816	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vmcSourceRemeasurement	VMCv20110909	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vmcdeepSource	VMCDEEPv20240506	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vvvSource	VVVDR1	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vvvSource	VVVDR2	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vvvSource	VVVDR5	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
ksppErrBits	vvvSource	VVVv20110718	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vvvSource, vvvSourceRemeasurement	VVVv20100531	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
ksppErrBits	vvvSynopticSource	VVVDR1	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vvvSynopticSource	VVVDR2	additional WFAU post-processing error bits in Ks	int	4		0	meta.code
			Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
ksppErrBits	vvvxSource	VVVXDR1	additional WFAU post-processing error bits in Ks	int	4		0	meta.code;em.IR.K
ksprobVar	sharksVariability	SHARKSv20210222	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	sharksVariability	SHARKSv20210421	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	ultravistaMapLcVariability	ULTRAVISTADR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	ultravistaVariability	ULTRAVISTADR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	videoVariability	VIDEODR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	videoVariability	VIDEODR3	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	videoVariability	VIDEODR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	videoVariability	VIDEODR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	videoVariability	VIDEOv20100513	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	videoVariability	VIDEOv20111208	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vikingVariability	VIKINGDR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vikingVariability	VIKINGv20110714	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vikingVariability	VIKINGv20111019	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCDR1	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCDR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCDR3	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCDR4	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCDR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20110816	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20110909	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20120126	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20121128	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20130304	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20130805	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20140428	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20140903	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20150309	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20151218	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20160311	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20160822	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20170109	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20170411	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20171101	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20180702	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20181120	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20191212	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20210708	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20230816	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcVariability	VMCv20240226	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcdeepVariability	VMCDEEPv20230713	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vmcdeepVariability	VMCDEEPv20240506	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vvvVariability	VVVDR1	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vvvVariability	VVVDR2	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vvvVariability	VVVDR5	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vvvVariability	VVVv20100531	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vvvVariability	VVVv20110718	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksprobVar	vvvxVariability	VVVXDR1	Probability of variable from chi-square (and other data)	real	4		-0.9999995e9	stat.probability;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksPsfMag	sharksSource	SHARKSv20210222	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	sharksSource	SHARKSv20210421	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSDR1	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vhsSource	VHSDR2	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vhsSource	VHSDR3	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSDR4	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSDR5	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSDR6	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20120926	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vhsSource	VHSv20130417	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vhsSource	VHSv20140409	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20150108	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20160114	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20160507	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20170630	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20180419	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20201209	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20231101	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vhsSource	VHSv20240731	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vikingSource	VIKINGDR2	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vikingSource	VIKINGDR3	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vikingSource	VIKINGDR4	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vikingSource	VIKINGv20110714	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vikingSource	VIKINGv20111019	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vikingSource	VIKINGv20130417	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vikingSource	VIKINGv20140402	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vikingSource	VIKINGv20150421	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vikingSource	VIKINGv20151230	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vikingSource	VIKINGv20160406	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vikingSource	VIKINGv20161202	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vikingSource	VIKINGv20170715	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCDR3	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCDR4	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20121128	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param
ksPsfMag	vmcPsfCatalogue	VMCv20140428	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20140903	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20150309	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20151218	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20160311	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20160822	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20170109	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20170411	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfCatalogue	VMCv20171101	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfDetections	VMCv20180702	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfDetections	VMCv20181120	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K
ksPsfMag	vmcPsfSource	VMCDR5	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcPsfSource	VMCv20180702	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcPsfSource	VMCv20181120	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcPsfSource	VMCv20191212	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcPsfSource	VMCv20210708	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcPsfSource	VMCv20230816	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcPsfSource	VMCv20240226	3 pixels PSF fitting magnitude Ks filter {catalogue TType keyword: Ks_MAG}	real	4	mag	-0.9999995e9	stat.fit.param;phot.mag;em.IR.K;meta.main
ksPsfMag	vmcSource	VMCDR1	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vmcSource	VMCDR2	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCDR3	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCDR4	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCDR5	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20110816	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vmcSource	VMCv20110909	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vmcSource	VMCv20120126	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vmcSource	VMCv20121128	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vmcSource	VMCv20130304	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag
ksPsfMag	vmcSource	VMCv20130805	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20140428	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20140903	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20150309	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20151218	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20160311	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20160822	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20170109	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20170411	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20171101	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20180702	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20181120	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20191212	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20210708	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20230816	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcSource	VMCv20240226	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcdeepSource	VMCDEEPv20230713	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vmcdeepSource	VMCDEEPv20240506	Point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksPsfMag	vvvPsfDaophotJKsSource	VVVDR5	PSF magnitude in Ks band {catalogue TType keyword: K}	real	4	mag	-0.9999995e9	instr.det.psf;phot.mag;em.IR.K;meta.main
ksPsfMag	vvvPsfDophotZYJHKsSource	VVVDR5	Mean PSF magnitude in KS band {catalogue TType keyword: mag_Ks}	real	4	mag	-0.9999995e9	instr.det.psf;phot.mag;em.IR.K;meta.main
ksPsfMagCor	vvvPsfDaophotJKsSource	VVVDR5	Reddening corrected PSF magnitude in Ks band (Ks_0) {catalogue TType keyword: cK}	real	4	mag	-0.9999995e9	phys.absorption;instr.det.psf;phot.mag;em.IR.K
ksPsfMagErr	sharksSource	SHARKSv20210222	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	sharksSource	SHARKSv20210421	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSDR1	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vhsSource	VHSDR2	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vhsSource	VHSDR3	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPsfMagErr	vhsSource	VHSDR4	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPsfMagErr	vhsSource	VHSDR5	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSDR6	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20120926	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vhsSource	VHSv20130417	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vhsSource	VHSv20140409	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPsfMagErr	vhsSource	VHSv20150108	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPsfMagErr	vhsSource	VHSv20160114	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20160507	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20170630	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20180419	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20201209	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20231101	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vhsSource	VHSv20240731	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGDR2	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGDR3	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGDR4	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPsfMagErr	vikingSource	VIKINGv20110714	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGv20111019	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGv20130417	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGv20140402	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vikingSource	VIKINGv20150421	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPsfMagErr	vikingSource	VIKINGv20151230	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vikingSource	VIKINGv20160406	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vikingSource	VIKINGv20161202	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vikingSource	VIKINGv20170715	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCDR3	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCDR4	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20121128	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcPsfCatalogue	VMCv20140428	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20140903	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20150309	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20151218	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20160311	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20160822	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20170109	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20170411	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfCatalogue	VMCv20171101	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfDetections	VMCv20181120	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcPsfDetections, vmcPsfSource	VMCv20180702	PSF error Ks filter {catalogue TType keyword: Ks_ERR}	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCDR1	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCDR2	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCDR3	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPsfMagErr	vmcSource	VMCDR4	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCDR5	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20110816	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCv20110909	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCv20120126	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCv20121128	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCv20130304	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCv20130805	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error
ksPsfMagErr	vmcSource	VMCv20140428	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksPsfMagErr	vmcSource	VMCv20140903	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPsfMagErr	vmcSource	VMCv20150309	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksPsfMagErr	vmcSource	VMCv20151218	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20160311	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20160822	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20170109	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20170411	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20171101	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20180702	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20181120	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20191212	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20210708	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20230816	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcSource	VMCv20240226	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcdeepSource	VMCDEEPv20230713	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vmcdeepSource	VMCDEEPv20240506	Error in point source profile-fitted Ks mag	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksPsfMagErr	vvvPsfDaophotJKsSource	VVVDR5	Error on PSF magnitude in Ks band {catalogue TType keyword: K_err}	real	4	mag	-0.9999995e9	stat.error;instr.det.psf;phot.mag;em.IR.K
ksPsfMagErr	vvvPsfDophotZYJHKsSource	VVVDR5	Error on mean PSF magnitude in KS band {catalogue TType keyword: er_Ks}	real	4	mag	-0.9999995e9	stat.error;instr.det.psf;em.IR.K
ksSeqNum	sharksSource	SHARKSv20210222	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	sharksSource	SHARKSv20210421	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	ultravistaSource	ULTRAVISTADR4	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSDR1	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vhsSource	VHSDR2	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vhsSource	VHSDR3	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSDR4	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSDR5	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSDR6	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20120926	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vhsSource	VHSv20130417	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vhsSource	VHSv20140409	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20150108	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20160114	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20160507	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20170630	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20180419	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vhsSource	VHSv20201209	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vhsSource	VHSv20231101	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vhsSource	VHSv20240731	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vhsSourceRemeasurement	VHSDR1	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	videoSource	VIDEODR2	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	videoSource	VIDEODR3	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	videoSource	VIDEODR4	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	videoSource	VIDEODR5	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	videoSource	VIDEOv20100513	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	videoSource	VIDEOv20111208	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	videoSourceRemeasurement	VIDEOv20100513	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vikingSource	VIKINGDR2	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vikingSource	VIKINGDR3	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vikingSource	VIKINGDR4	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vikingSource	VIKINGv20110714	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vikingSource	VIKINGv20111019	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vikingSource	VIKINGv20130417	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vikingSource	VIKINGv20140402	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vikingSource	VIKINGv20150421	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vikingSource	VIKINGv20151230	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vikingSource	VIKINGv20160406	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vikingSource	VIKINGv20161202	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vikingSource	VIKINGv20170715	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vikingSourceRemeasurement	VIKINGv20110714	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vikingSourceRemeasurement	VIKINGv20111019	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vmcSource	VMCDR2	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vmcSource	VMCDR3	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCDR4	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCDR5	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vmcSource	VMCv20110816	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vmcSource	VMCv20110909	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vmcSource	VMCv20120126	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vmcSource	VMCv20121128	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vmcSource	VMCv20130304	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vmcSource	VMCv20130805	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vmcSource	VMCv20140428	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20140903	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20150309	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20151218	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20160311	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20160822	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20170109	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20170411	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20171101	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20180702	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20181120	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vmcSource	VMCv20191212	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vmcSource	VMCv20210708	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vmcSource	VMCv20230816	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vmcSource	VMCv20240226	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vmcSource, vmcSynopticSource	VMCDR1	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vmcSourceRemeasurement	VMCv20110816	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vmcSourceRemeasurement	VMCv20110909	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vmcdeepSource	VMCDEEPv20240506	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSeqNum	vvvSource	VVVDR2	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vvvSource	VVVDR5	the running number of the Ks detection	int	4		-99999999	meta.number;em.IR.K
ksSeqNum	vvvSource	VVVv20100531	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vvvSource	VVVv20110718	the running number of the Ks detection	int	4		-99999999	meta.id
ksSeqNum	vvvSource, vvvSynopticSource	VVVDR1	the running number of the Ks detection	int	4		-99999999	meta.number
ksSeqNum	vvvSourceRemeasurement	VVVv20100531	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vvvSourceRemeasurement	VVVv20110718	the running number of the Ks remeasurement	int	4		-99999999	meta.id
ksSeqNum	vvvxSource	VVVXDR1	the running number of the Ks detection	int	4		-99999999	meta.id;em.IR.K
ksSerMag2D	sharksSource	SHARKSv20210222	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	sharksSource	SHARKSv20210421	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSDR1	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vhsSource	VHSDR2	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vhsSource	VHSDR3	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSDR4	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSDR5	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSDR6	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20120926	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vhsSource	VHSv20130417	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vhsSource	VHSv20140409	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20150108	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20160114	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20160507	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20170630	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20180419	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20201209	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20231101	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vhsSource	VHSv20240731	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vikingSource	VIKINGDR2	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vikingSource	VIKINGDR3	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vikingSource	VIKINGDR4	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vikingSource	VIKINGv20110714	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vikingSource	VIKINGv20111019	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vikingSource	VIKINGv20130417	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vikingSource	VIKINGv20140402	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vikingSource	VIKINGv20150421	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vikingSource	VIKINGv20151230	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vikingSource	VIKINGv20160406	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vikingSource	VIKINGv20161202	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vikingSource	VIKINGv20170715	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCDR1	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vmcSource	VMCDR2	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCDR3	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCDR4	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCDR5	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20110816	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vmcSource	VMCv20110909	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vmcSource	VMCv20120126	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vmcSource	VMCv20121128	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vmcSource	VMCv20130304	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag
ksSerMag2D	vmcSource	VMCv20130805	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20140428	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20140903	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20150309	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20151218	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20160311	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20160822	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20170109	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20170411	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20171101	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20180702	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20181120	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20191212	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20210708	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20230816	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcSource	VMCv20240226	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcdeepSource	VMCDEEPv20230713	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2D	vmcdeepSource	VMCDEEPv20240506	Extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	phot.mag;em.IR.K
ksSerMag2DErr	sharksSource	SHARKSv20210222	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	sharksSource	SHARKSv20210421	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSDR1	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vhsSource	VHSDR2	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vhsSource	VHSDR3	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksSerMag2DErr	vhsSource	VHSDR4	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksSerMag2DErr	vhsSource	VHSDR5	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSDR6	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20120926	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vhsSource	VHSv20130417	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vhsSource	VHSv20140409	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20150108	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksSerMag2DErr	vhsSource	VHSv20160114	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20160507	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20170630	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20180419	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20201209	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20231101	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vhsSource	VHSv20240731	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	videoSource	VIDEOv20100513	Not available in SE output	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGDR2	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGDR3	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGDR4	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksSerMag2DErr	vikingSource	VIKINGv20110714	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGv20111019	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGv20130417	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGv20140402	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vikingSource	VIKINGv20150421	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksSerMag2DErr	vikingSource	VIKINGv20151230	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vikingSource	VIKINGv20160406	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vikingSource	VIKINGv20161202	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vikingSource	VIKINGv20170715	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCDR1	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCDR2	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCDR3	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksSerMag2DErr	vmcSource	VMCDR4	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCDR5	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20110816	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCv20110909	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCv20120126	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCv20121128	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCv20130304	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCv20130805	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error
ksSerMag2DErr	vmcSource	VMCv20140428	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20140903	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksSerMag2DErr	vmcSource	VMCv20150309	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;em.IR.K;phot.mag
ksSerMag2DErr	vmcSource	VMCv20151218	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20160311	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20160822	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20170109	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20170411	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20171101	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20180702	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20181120	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20191212	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20210708	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20230816	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcSource	VMCv20240226	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcdeepSource	VMCDEEPv20230713	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSerMag2DErr	vmcdeepSource	VMCDEEPv20240506	Error in extended source Ks mag (profile-fitted)	real	4	mag	-0.9999995e9	stat.error;phot.mag;em.IR.K
ksSharp	vmcPsfCatalogue	VMCDR3	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCDR4	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20121128	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param
ksSharp	vmcPsfCatalogue	VMCv20140428	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20140903	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20150309	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20151218	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20160311	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20160822	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20170109	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20170411	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfCatalogue	VMCv20171101	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfDetections	VMCv20181120	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksSharp	vmcPsfDetections, vmcPsfSource	VMCv20180702	PSF fitting shape parameter Ks filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: Ks_SHARP}	real	4	dex	-0.9999995e9	stat.fit.param;em.IR.K
ksskewness	sharksVariability	SHARKSv20210222	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	sharksVariability	SHARKSv20210421	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	ultravistaMapLcVariability	ULTRAVISTADR4	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	ultravistaVariability	ULTRAVISTADR4	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	videoVariability	VIDEODR2	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	videoVariability	VIDEODR3	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	videoVariability	VIDEODR4	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	videoVariability	VIDEODR5	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	videoVariability	VIDEOv20100513	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	videoVariability	VIDEOv20111208	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vikingVariability	VIKINGDR2	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vikingVariability	VIKINGv20110714	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vikingVariability	VIKINGv20111019	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCDR1	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCDR2	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCDR3	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCDR4	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCDR5	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20110816	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20110909	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20120126	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20121128	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20130304	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20130805	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20140428	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20140903	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20150309	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20151218	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20160311	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20160822	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20170109	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20170411	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20171101	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20180702	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20181120	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20191212	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20210708	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20230816	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcVariability	VMCv20240226	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcdeepVariability	VMCDEEPv20230713	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vmcdeepVariability	VMCDEEPv20240506	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vvvVariability	VVVDR1	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vvvVariability	VVVDR2	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.NIR
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vvvVariability	VVVDR5	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vvvVariability	VVVv20100531	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vvvVariability	VVVv20110718	Skewness in Ks band (see Sesar et al. 2007)	real	4		-0.9999995e9
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksskewness	vvvxVariability	VVVXDR1	Skewness in Ks band (see Sesar et al. 2007)	real	4	mag	-0.9999995e9	stat.param;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
kstotalPeriod	sharksVariability	SHARKSv20210222	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	sharksVariability	SHARKSv20210421	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	ultravistaMapLcVariability	ULTRAVISTADR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	ultravistaVariability	ULTRAVISTADR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	videoVariability	VIDEODR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	videoVariability	VIDEODR3	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	videoVariability	VIDEODR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	videoVariability	VIDEODR5	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	videoVariability	VIDEOv20100513	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	videoVariability	VIDEOv20111208	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vikingVariability	VIKINGDR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vikingVariability	VIKINGv20110714	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vikingVariability	VIKINGv20111019	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCDR1	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCDR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCDR3	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCDR4	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCDR5	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20110816	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20110909	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20120126	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20121128	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20130304	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20130805	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20140428	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration;em.IR.K
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20140903	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20150309	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20151218	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20160311	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20160822	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20170109	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20170411	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20171101	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20180702	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20181120	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20191212	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20210708	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20230816	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcVariability	VMCv20240226	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcdeepVariability	VMCDEEPv20230713	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vmcdeepVariability	VMCDEEPv20240506	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vvvVariability	VVVDR1	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vvvVariability	VVVDR2	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vvvVariability	VVVDR5	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vvvVariability	VVVv20100531	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vvvVariability	VVVv20110718	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
kstotalPeriod	vvvxVariability	VVVXDR1	total period of observations (last obs-first obs)	real	4	days	-0.9999995e9	time.duration
			The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
ksVarClass	sharksVariability	SHARKSv20210222	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	sharksVariability	SHARKSv20210421	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	ultravistaMapLcVariability	ULTRAVISTADR4	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	ultravistaVariability	ULTRAVISTADR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	videoVariability	VIDEODR2	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	videoVariability	VIDEODR3	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	videoVariability	VIDEODR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	videoVariability	VIDEODR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	videoVariability	VIDEOv20100513	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	videoVariability	VIDEOv20111208	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vikingVariability	VIKINGDR2	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vikingVariability	VIKINGv20110714	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vikingVariability	VIKINGv20111019	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCDR1	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCDR2	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCDR3	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCDR4	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCDR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20110816	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20110909	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20120126	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20121128	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20130304	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20130805	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20140428	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20140903	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20150309	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20151218	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20160311	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20160822	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20170109	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20170411	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20171101	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20180702	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20181120	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20191212	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20210708	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20230816	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcVariability	VMCv20240226	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcdeepVariability	VMCDEEPv20230713	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vmcdeepVariability	VMCDEEPv20240506	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vvvVariability	VVVDR1	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vvvVariability	VVVDR2	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vvvVariability	VVVDR5	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vvvVariability	VVVv20100531	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vvvVariability	VVVv20110718	Classification of variability in this band	smallint	2		-9999
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksVarClass	vvvxVariability	VVVXDR1	Classification of variability in this band	smallint	2		-9999	meta.code.class;src.var;em.IR.K
			The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
ksXi	sharksSource	SHARKSv20210222	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	sharksSource	SHARKSv20210421	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	ultravistaSource	ULTRAVISTADR4	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSDR1	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSDR2	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSDR3	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSDR4	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSDR5	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSDR6	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20120926	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20130417	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20140409	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20150108	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20160114	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20160507	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20170630	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20180419	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20201209	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20231101	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vhsSource	VHSv20240731	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	videoSource	VIDEODR2	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	videoSource	VIDEODR3	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	videoSource	VIDEODR4	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	videoSource	VIDEODR5	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	videoSource	VIDEOv20100513	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	videoSource	VIDEOv20111208	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGDR2	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGDR3	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGDR4	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20110714	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20111019	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20130417	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20140402	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20150421	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20151230	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20160406	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20161202	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vikingSource	VIKINGv20170715	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCDR2	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCDR3	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCDR4	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCDR5	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20110816	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20110909	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20120126	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20121128	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20130304	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20130805	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20140428	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20140903	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20150309	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20151218	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20160311	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20160822	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20170109	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20170411	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20171101	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20180702	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20181120	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20191212	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20210708	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20230816	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource	VMCv20240226	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcSource, vmcSynopticSource	VMCDR1	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcdeepSource	VMCDEEPv20240506	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vvvSource	VVVDR2	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vvvSource	VVVDR5	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vvvSource	VVVv20100531	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vvvSource	VVVv20110718	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vvvSource, vvvSynopticSource	VVVDR1	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
ksXi	vvvxSource	VVVXDR1	Offset of Ks detection from master position (+east/-west)	real	4	arcsec	-0.9999995e9	pos.eq.ra;arith.diff;em.IR.K
			When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
kurtosis	phot_variable_time_series_g_fov_statistical_parameters	GAIADR1	Standardized unweighted kurtosis of the G-band time series values	float	8			stat.value