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Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20240329 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20240329 Schema Browser (other Browser versions).
A B C D E F G H I J K L M
N O P Q R S T U V W X Y Z

K

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified 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, 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 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, 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 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, 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 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, 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 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, 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 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_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_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_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_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_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_1AverageConf vvvSource VVVDR5 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_1ClassStat vvvSource VVVDR5 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_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_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_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_1Gausig vvvSource VVVDR5 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_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_1PA vvvSource VVVDR5 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_1SeqNum vvvSource VVVDR5 the running number of the Ks_1 detection int 4   -99999999 meta.number;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_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_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_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_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_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_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_2AverageConf vvvSource VVVDR5 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_2ClassStat vvvSource VVVDR5 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_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_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_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_2Gausig vvvSource VVVDR5 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_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_2PA vvvSource VVVDR5 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_2SeqNum vvvSource VVVDR5 the running number of the Ks_2 detection int 4   -99999999 meta.number;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.
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
ksAmpl vmcRRlyraeVariables VMCv20240226 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 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
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 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
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 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 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 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 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 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  
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 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, 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  
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 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 vmcdeepSynopticSource VMCDEEPv20230713 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
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 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 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 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
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 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 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 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
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 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
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 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
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 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
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 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
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 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.
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 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.
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 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, 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
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 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)
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 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.
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 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.
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 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.
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 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.
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 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.
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, 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, 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
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, 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, 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
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 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.
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 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.
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, 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, 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
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 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, 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
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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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 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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object'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.
64Memory overflow occurred during deblending
128Memory 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, 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
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 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, 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.
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 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  
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 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.
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, 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, 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
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 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 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.
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 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  
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 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  
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 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.
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 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.
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 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.
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 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.
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
ksMean vmcRRlyraeVariables VMCv20240226 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
ksMeanErr vmcRRlyraeVariables VMCv20240226 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 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.
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 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.
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 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.
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 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, 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
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 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.
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 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.
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 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 vmcdeepSynopticSource VMCDEEPv20230713 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
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 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.
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 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.
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 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.
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 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.
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 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.
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 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.
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 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.
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, 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, 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
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 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
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 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
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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, 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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
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.
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 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.
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 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 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 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 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 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, 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
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 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
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 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
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 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.
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 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.
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 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.
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 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, 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.
kurtosis phot_variable_time_series_g_fov_statistical_parameters GAIADR1 Standardized unweighted kurtosis of the G-band time series values float 8     stat.value


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29/03/2024