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Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20150413 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20150413 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    
k_2mrat twomass_scn 2MASS Ks-band average 2nd image moment ratio. real 4     stat.fit.param
k_2mrat twomass_sixx2_scn 2MASS K band average 2nd image moment ratio for scan real 4      
k_5sig_ba twomass_xsc 2MASS K minor/major axis ratio fit to the 5-sigma isophote. real 4     phys.size.axisRatio
k_5sig_phi twomass_xsc 2MASS K angle to 5-sigma major axis (E of N). smallint 2 degrees   stat.error
k_5surf twomass_xsc 2MASS K central surface brightness (r<=5). real 4 mag   phot.mag.sb
k_ba twomass_sixx2_xsc 2MASS K minor/major axis ratio fit to the 3-sigma isophote real 4      
k_ba twomass_xsc 2MASS K minor/major axis ratio fit to the 3-sigma isophote. real 4     phys.size.axisRatio
k_back twomass_xsc 2MASS K coadd median background. real 4     meta.code
k_bisym_chi twomass_xsc 2MASS K bi-symmetric cross-correlation chi. real 4     stat.fit.param
k_bisym_rat twomass_xsc 2MASS K bi-symmetric flux ratio. real 4     phot.flux;arith.ratio
k_bndg_amp twomass_xsc 2MASS K banding maximum FT amplitude on this side of coadd. real 4 DN   stat.fit.param
k_bndg_per twomass_xsc 2MASS K banding Fourier Transf. period on this side of coadd. int 4 arcsec   stat.fit.param
k_chif_ellf twomass_xsc 2MASS K % chi-fraction for elliptical fit to 3-sig isophote. real 4     stat.fit.param
k_cmsig twomass_psc 2MASS Corrected photometric uncertainty for the default Ks-band magnitude. real 4 mag Ks-band phot.flux
k_con_indx twomass_xsc 2MASS K concentration index r_75%/r_25%. real 4     phys.size;arith.ratio
k_d_area twomass_xsc 2MASS K 5-sigma to 3-sigma differential area. smallint 2     stat.fit.residual
k_flg_10 twomass_xsc 2MASS K confusion flag for 10 arcsec circular ap. mag. smallint 2     meta.code
k_flg_15 twomass_xsc 2MASS K confusion flag for 15 arcsec circular ap. mag. smallint 2     meta.code
k_flg_20 twomass_xsc 2MASS K confusion flag for 20 arcsec circular ap. mag. smallint 2     meta.code
k_flg_25 twomass_xsc 2MASS K confusion flag for 25 arcsec circular ap. mag. smallint 2     meta.code
k_flg_30 twomass_xsc 2MASS K confusion flag for 30 arcsec circular ap. mag. smallint 2     meta.code
k_flg_40 twomass_xsc 2MASS K confusion flag for 40 arcsec circular ap. mag. smallint 2     meta.code
k_flg_5 twomass_xsc 2MASS K confusion flag for 5 arcsec circular ap. mag. smallint 2     meta.code
k_flg_50 twomass_xsc 2MASS K confusion flag for 50 arcsec circular ap. mag. smallint 2     meta.code
k_flg_60 twomass_xsc 2MASS K confusion flag for 60 arcsec circular ap. mag. smallint 2     meta.code
k_flg_7 twomass_sixx2_xsc 2MASS K confusion flag for 7 arcsec circular ap. mag smallint 2      
k_flg_7 twomass_xsc 2MASS K confusion flag for 7 arcsec circular ap. mag. smallint 2     meta.code
k_flg_70 twomass_xsc 2MASS K confusion flag for 70 arcsec circular ap. mag. smallint 2     meta.code
k_flg_c twomass_xsc 2MASS K confusion flag for Kron circular mag. smallint 2     meta.code
k_flg_e twomass_xsc 2MASS K confusion flag for Kron elliptical mag. smallint 2     meta.code
k_flg_fc twomass_xsc 2MASS K confusion flag for fiducial Kron circ. mag. smallint 2     meta.code
k_flg_fe twomass_xsc 2MASS K confusion flag for fiducial Kron ell. mag. smallint 2     meta.code
k_flg_i20c twomass_xsc 2MASS K confusion flag for 20mag/sq." iso. circ. mag. smallint 2     meta.code
k_flg_i20e twomass_xsc 2MASS K confusion flag for 20mag/sq." iso. ell. mag. smallint 2     meta.code
k_flg_i21c twomass_xsc 2MASS K confusion flag for 21mag/sq." iso. circ. mag. smallint 2     meta.code
k_flg_i21e twomass_xsc 2MASS K confusion flag for 21mag/sq." iso. ell. mag. smallint 2     meta.code
k_flg_j21fc twomass_xsc 2MASS K confusion flag for 21mag/sq." iso. fid. circ. mag. smallint 2     meta.code
k_flg_j21fe twomass_xsc 2MASS K confusion flag for 21mag/sq." iso. fid. ell. mag. smallint 2     meta.code
k_flg_k20fc twomass_xsc 2MASS K confusion flag for 20mag/sq." iso. fid. circ. mag. smallint 2     meta.code
k_flg_k20fe twomass_sixx2_xsc 2MASS K confusion flag for 20mag/sq.″ iso. fid. ell. mag smallint 2      
k_flg_k20fe twomass_xsc 2MASS K confusion flag for 20mag/sq." iso. fid. ell. mag. smallint 2     meta.code
k_m twomass_psc 2MASS Default Ks-band magnitude real 4 mag   phot.flux
k_m twomass_sixx2_psc 2MASS K selected "default" magnitude real 4 mag    
k_m_10 twomass_xsc 2MASS K 10 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_15 twomass_xsc 2MASS K 15 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_20 twomass_xsc 2MASS K 20 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_25 twomass_xsc 2MASS 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 2MASS K 30 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_40 twomass_xsc 2MASS K 40 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_5 twomass_xsc 2MASS K 5 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_50 twomass_xsc 2MASS K 50 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_60 twomass_xsc 2MASS K 60 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_7 twomass_sixx2_xsc 2MASS K 7 arcsec radius circular aperture magnitude real 4 mag    
k_m_7 twomass_xsc 2MASS K 7 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_70 twomass_xsc 2MASS K 70 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
k_m_c twomass_xsc 2MASS K Kron circular aperture magnitude. real 4 mag   phot.flux
k_m_e twomass_xsc 2MASS K Kron elliptical aperture magnitude. real 4 mag   phot.flux
k_m_ext twomass_sixx2_xsc 2MASS K mag from fit extrapolation real 4 mag    
k_m_ext twomass_xsc 2MASS K mag from fit extrapolation. real 4 mag   phot.flux
k_m_fc twomass_xsc 2MASS K fiducial Kron circular magnitude. real 4 mag   phot.flux
k_m_fe twomass_xsc 2MASS K fiducial Kron ell. mag aperture magnitude. real 4 mag   phot.flux
k_m_i20c twomass_xsc 2MASS K 20mag/sq." isophotal circular ap. magnitude. real 4 mag   phot.flux
k_m_i20e twomass_xsc 2MASS K 20mag/sq." isophotal elliptical ap. magnitude. real 4 mag   phot.flux
k_m_i21c twomass_xsc 2MASS K 21mag/sq." isophotal circular ap. magnitude. real 4 mag   phot.flux
k_m_i21e twomass_xsc 2MASS K 21mag/sq." isophotal elliptical ap. magnitude. real 4 mag   phot.flux
k_m_j21fc twomass_xsc 2MASS K 21mag/sq." isophotal fiducial circ. ap. mag. real 4 mag   phot.flux
k_m_j21fe twomass_xsc 2MASS K 21mag/sq." isophotal fiducial ell. ap. magnitude. real 4 mag   phot.flux
k_m_k20fc twomass_xsc 2MASS 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 2MASS K 20mag/sq.″ isophotal fiducial ell. ap. magnitude real 4 mag    
k_m_k20fe twomass_xsc 2MASS K 20mag/sq." isophotal fiducial ell. ap. magnitude. real 4 mag   phot.flux
k_m_stdap twomass_psc 2MASS Ks-band "standard" aperture magnitude. real 4 mag   phot.flux
k_m_sys twomass_xsc 2MASS K system photometry magnitude. real 4 mag   phot.flux
k_mnsurfb_eff twomass_xsc 2MASS K mean surface brightness at the half-light radius. real 4 mag   phot.mag.sb
k_msig twomass_sixx2_psc 2MASS K "default" mag uncertainty real 4 mag    
k_msig_10 twomass_xsc 2MASS K 1-sigma uncertainty in 10 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_15 twomass_xsc 2MASS K 1-sigma uncertainty in 15 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_20 twomass_xsc 2MASS K 1-sigma uncertainty in 20 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_25 twomass_xsc 2MASS 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 2MASS K 1-sigma uncertainty in 30 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_40 twomass_xsc 2MASS K 1-sigma uncertainty in 40 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_5 twomass_xsc 2MASS K 1-sigma uncertainty in 5 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_50 twomass_xsc 2MASS K 1-sigma uncertainty in 50 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_60 twomass_xsc 2MASS K 1-sigma uncertainty in 60 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_7 twomass_sixx2_xsc 2MASS K 1-sigma uncertainty in 7 arcsec circular ap. mag real 4 mag    
k_msig_7 twomass_xsc 2MASS K 1-sigma uncertainty in 7 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_70 twomass_xsc 2MASS K 1-sigma uncertainty in 70 arcsec circular ap. mag. real 4 mag   stat.error
k_msig_c twomass_xsc 2MASS K 1-sigma uncertainty in Kron circular mag. real 4 mag   stat.error
k_msig_e twomass_xsc 2MASS K 1-sigma uncertainty in Kron elliptical mag. real 4 mag   stat.error
k_msig_ext twomass_sixx2_xsc 2MASS K 1-sigma uncertainty in mag from fit extrapolation real 4 mag    
k_msig_ext twomass_xsc 2MASS K 1-sigma uncertainty in mag from fit extrapolation. real 4 mag   stat.error
k_msig_fc twomass_xsc 2MASS K 1-sigma uncertainty in fiducial Kron circ. mag. real 4 mag   stat.error
k_msig_fe twomass_xsc 2MASS K 1-sigma uncertainty in fiducial Kron ell. mag. real 4 mag   stat.error
k_msig_i20c twomass_xsc 2MASS K 1-sigma uncertainty in 20mag/sq." iso. circ. mag. real 4 mag   stat.error
k_msig_i20e twomass_xsc 2MASS K 1-sigma uncertainty in 20mag/sq." iso. ell. mag. real 4 mag   stat.error
k_msig_i21c twomass_xsc 2MASS K 1-sigma uncertainty in 21mag/sq." iso. circ. mag. real 4 mag   stat.error
k_msig_i21e twomass_xsc 2MASS K 1-sigma uncertainty in 21mag/sq." iso. ell. mag. real 4 mag   stat.error
k_msig_j21fc twomass_xsc 2MASS K 1-sigma uncertainty in 21mag/sq." iso.fid.circ.mag. real 4 mag   stat.error
k_msig_j21fe twomass_xsc 2MASS K 1-sigma uncertainty in 21mag/sq." iso.fid.ell.mag. real 4 mag   stat.error
k_msig_k20fc twomass_xsc 2MASS K 1-sigma uncertainty in 20mag/sq." iso.fid.circ. mag. real 4 mag   stat.error
k_msig_k20fe twomass_sixx2_xsc 2MASS K 1-sigma uncertainty in 20mag/sq.″ iso.fid.ell.mag real 4 mag    
k_msig_k20fe twomass_xsc 2MASS K 1-sigma uncertainty in 20mag/sq." iso.fid.ell.mag. real 4 mag   stat.error
k_msig_stdap twomass_psc 2MASS Uncertainty in the Ks-band standard aperture magnitude. real 4 mag   phot.flux
k_msig_sys twomass_xsc 2MASS K 1-sigma uncertainty in system photometry mag. real 4 mag   stat.error
k_msigcom twomass_psc 2MASS Combined, or total photometric uncertainty for the default Ks-band magnitude. real 4 mag Ks-band phot.flux
k_msigcom twomass_sixx2_psc 2MASS combined (total) K band photometric uncertainty real 4 mag    
k_msnr10 twomass_scn 2MASS The estimated Ks-band magnitude at which SNR=10 is achieved for this scan. real 4 mag   phot.flux
k_msnr10 twomass_sixx2_scn 2MASS K mag at which SNR=10 is achieved, from k_psp and k_zp_ap real 4 mag    
k_n_snr10 twomass_scn 2MASS Number of point sources at Ks-band with SNR>10 (instrumental mag <=14.3) int 4     meta.number
k_n_snr10 twomass_sixx2_scn 2MASS number of K point sources with SNR>10 (instrumental m<=14.3) int 4      
k_pchi twomass_xsc 2MASS K chi^2 of fit to rad. profile (LCSB: alpha scale len). real 4     stat.fit.param
k_peak twomass_xsc 2MASS K peak pixel brightness. real 4 mag   phot.mag.sb
k_perc_darea twomass_xsc 2MASS K 5-sigma to 3-sigma percent area change. smallint 2     FIT_PARAM
k_phi twomass_sixx2_xsc 2MASS K angle to 3-sigma major axis (E of N) smallint 2 deg    
k_phi twomass_xsc 2MASS K angle to 3-sigma major axis (E of N). smallint 2 degrees   pos.posAng
k_psfchi twomass_psc 2MASS 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 2MASS Ks-band photometric sensitivity paramater (PSP). real 4     instr.sensitivity
k_psp twomass_sixx2_scn 2MASS K photometric sensitivity param: k_shape_avg*(k_fbg_avg^.29) real 4      
k_pts_noise twomass_scn 2MASS 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 2MASS log10 of K band modal point src noise estimate real 4 logmJy    
k_r_c twomass_xsc 2MASS K Kron circular aperture radius. real 4 arcsec   phys.angSize;src
k_r_e twomass_xsc 2MASS K Kron elliptical aperture semi-major axis. real 4 arcsec   phys.angSize;src
k_r_eff twomass_xsc 2MASS K half-light (integrated half-flux point) radius. real 4 arcsec   phys.angSize;src
k_r_i20c twomass_xsc 2MASS K 20mag/sq." isophotal circular aperture radius. real 4 arcsec   phys.angSize;src
k_r_i20e twomass_xsc 2MASS K 20mag/sq." isophotal elliptical ap. semi-major axis. real 4 arcsec   phys.angSize;src
k_r_i21c twomass_xsc 2MASS K 21mag/sq." isophotal circular aperture radius. real 4 arcsec   phys.angSize;src
k_r_i21e twomass_xsc 2MASS K 21mag/sq." isophotal elliptical ap. semi-major axis. real 4 arcsec   phys.angSize;src
k_resid_ann twomass_xsc 2MASS K residual annulus background median. real 4 DN   meta.code
k_sc_1mm twomass_xsc 2MASS K 1st moment (score) (LCSB: super blk 2,4,8 SNR). real 4     meta.code
k_sc_2mm twomass_xsc 2MASS K 2nd moment (score) (LCSB: SNRMAX - super SNR max). real 4     meta.code
k_sc_msh twomass_xsc 2MASS K median shape score. real 4     meta.code
k_sc_mxdn twomass_xsc 2MASS K mxdn (score) (LCSB: BSNR - block/smoothed SNR). real 4     meta.code
k_sc_r1 twomass_xsc 2MASS K r1 (score). real 4     meta.code
k_sc_r23 twomass_xsc 2MASS K r23 (score) (LCSB: TSNR - integrated SNR for r=15). real 4     meta.code
k_sc_sh twomass_xsc 2MASS K shape (score). real 4     meta.code
k_sc_vint twomass_xsc 2MASS K vint (score). real 4     meta.code
k_sc_wsh twomass_xsc 2MASS K wsh (score) (LCSB: PSNR - peak raw SNR). real 4     meta.code
k_seetrack twomass_xsc 2MASS K band seetracking score. real 4     meta.code
k_sh0 twomass_xsc 2MASS K ridge shape (LCSB: BSNR limit). real 4     FIT_PARAM
k_shape_avg twomass_scn 2MASS Ks-band average seeing shape for scan. real 4     instr.obsty.seeing
k_shape_avg twomass_sixx2_scn 2MASS K band average seeing shape for scan real 4      
k_shape_rms twomass_scn 2MASS RMS-error of Ks-band average seeing shape. real 4     instr.obsty.seeing
k_shape_rms twomass_sixx2_scn 2MASS rms of K band avg seeing shape for scan real 4      
k_sig_sh0 twomass_xsc 2MASS K ridge shape sigma (LCSB: B2SNR limit). real 4     FIT_PARAM
k_snr twomass_psc 2MASS Ks-band "scan" signal-to-noise ratio. real 4 mag   instr.det.noise
k_snr twomass_sixx2_psc 2MASS K band "scan" signal-to-noise ratio real 4      
k_subst2 twomass_xsc 2MASS K residual background #2 (score). real 4     meta.code
k_zp_ap twomass_scn 2MASS Photometric zero-point for Ks-band aperture photometry. real 4 mag   phot.mag;arith.zp
k_zp_ap twomass_sixx2_scn 2MASS K band ap. calibration photometric zero-point for scan real 4 mag    
k_zperr_ap twomass_scn 2MASS RMS-error of zero-point for Ks-band aperture photometry real 4 mag   stat.error
k_zperr_ap twomass_sixx2_scn 2MASS K band ap. calibration rms error of zero-point for scan real 4 mag    
KBESTR spectra SIXDF cross-correlation template int 4      
KEXT twomass SIXDF KEXT magnitude real 4 mag    
KEXT_K twomass SIXDF KEXT minus K (corrected) real 4 mag    
Kmag mcps_lmcSource, mcps_smcSource MCPS The K' band magnitude (from 2MASS) (0.00 if star not detected.) real 4 mag    
kMag ukirtFSstars SVNGC253v20100429 K band total magnitude on the MKO(UFTI) system real 4 mag   phot.mag
kMag ukirtFSstars SVORIONv20100429 K band total magnitude on the MKO(UFTI) system real 4 mag   phot.mag
kMag ukirtFSstars ULTRAVISTAv20100429 K band total magnitude on the MKO(UFTI) system real 4 mag   phot.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    
kMagErr ukirtFSstars SVNGC253v20100429 K band magnitude error real 4 mag   stat.error
kMagErr ukirtFSstars SVORIONv20100429 K band magnitude error real 4 mag   stat.error
kMagErr ukirtFSstars ULTRAVISTAv20100429 K band magnitude error real 4 mag   stat.error
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 svNgc253Detection SVNGC253v20100429 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
kronFlux svOrionDetection SVORIONv20100429 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
kronFlux ultravistaDetection ULTRAVISTAv20100429 flux within Kron radius circular aperture (SE: FLUX_AUTO) {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
kronFlux vhsDetection VHSDR1 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
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 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 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 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 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 VIKINGv20110714 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
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 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 VMCv20110816 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
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 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, vvvListRemeasurement VVVv20100531 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
kronFlux vvvListRemeasurement VVVv20110718 flux within circular aperture to k × r_k ; k = 2 {catalogue TType keyword: Kron_flux} real 4 ADU   phot.count;em.opt
kronFluxErr svNgc253Detection SVNGC253v20100429 error on Kron flux {catalogue TType keyword: Kron_flux_err} real 4 ADU   stat.error
kronFluxErr svOrionDetection SVORIONv20100429 error on Kron flux {catalogue TType keyword: Kron_flux_err} real 4 ADU   stat.error
kronFluxErr ultravistaDetection ULTRAVISTAv20100429 error on Kron flux (SE: FLUXERR_AUTO) {catalogue TType keyword: Kron_flux_err} real 4 ADU   stat.error
kronFluxErr vhsDetection VHSDR1 error on Kron flux {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 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 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 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 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 VIKINGv20110714 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 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 VMCv20110816 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 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, vvvListRemeasurement VVVv20100531 error on Kron flux {catalogue TType keyword: Kron_flux_err} real 4 ADU   stat.error
kronFluxErr vvvListRemeasurement VVVv20110718 error on Kron flux {catalogue TType keyword: Kron_flux_err} real 4 ADU   stat.error
kronMag svNgc253Detection SVNGC253v20100429 Calibrated Kron magnitude within circular aperture r_k real 4 mag   phot.mag
kronMag svOrionDetection SVORIONv20100429 Calibrated Kron magnitude within circular aperture r_k real 4 mag   phot.mag
kronMag ultravistaDetection ULTRAVISTAv20100429 Calibrated Kron magnitude within circular aperture r_k real 4 mag   phot.mag
kronMag vhsDetection VHSDR1 Calibrated Kron magnitude within circular aperture r_k 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 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 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 VIDEOv20100513 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 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 VIKINGv20110714 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 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 VMCv20110816 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 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, vvvListRemeasurement VVVv20100531 Calibrated Kron magnitude within circular aperture r_k real 4 mag   phot.mag
kronMag vvvListRemeasurement VVVv20110718 Calibrated Kron magnitude within circular aperture r_k real 4 mag   phot.mag
kronMagErr svNgc253Detection SVNGC253v20100429 error on calibrated Kron magnitude real 4 mag   stat.error
kronMagErr svOrionDetection SVORIONv20100429 error on calibrated Kron magnitude real 4 mag   stat.error
kronMagErr ultravistaDetection ULTRAVISTAv20100429 error on calibrated Kron magnitude real 4 mag   stat.error
kronMagErr vhsDetection VHSDR1 error on calibrated Kron magnitude real 4 mag   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 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 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 VIDEOv20100513 error on calibrated Kron magnitude real 4 mag   stat.error
kronMagErr videoDetection VIDEOv20111208 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 VIKINGv20110714 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 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 VMCv20110816 error on calibrated Kron magnitude real 4 mag   stat.error
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 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, vvvListRemeasurement VVVv20100531 error on calibrated Kron magnitude real 4 mag   stat.error
kronMagErr vvvListRemeasurement VVVv20110718 error on calibrated Kron magnitude real 4 mag   stat.error
kronRad svNgc253Detection SVNGC253v20100429 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 svOrionDetection SVORIONv20100429 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 ultravistaDetection ULTRAVISTAv20100429 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 vhsDetection 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 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 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 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 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 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 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 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 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 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 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 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, 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
kronRad vvvListRemeasurement VVVv20110718 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
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 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
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 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 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 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 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 vvvSource VVVDR1 Error in extended source Ks mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
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 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 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 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 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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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 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 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 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 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 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
ksAperMag3Err svNgc253Source SVNGC253v20100429 Error in default point/extended source Ks mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
ksAperMag3Err svOrionSource SVORIONv20100429 Error in default point/extended source Ks mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
ksAperMag3Err ultravistaSource ULTRAVISTAv20100429 Error in default point/extended source Ks mag (2.0 arcsec aperture diameter) 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 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 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 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 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 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, vmcSynopticSource VMCDR1 Error in default point/extended source Ks mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
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 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
ksAperMag4 svNgc253Source SVNGC253v20100429 Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
ksAperMag4 svOrionSource SVORIONv20100429 Extended source Ks aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
ksAperMag4 ultravistaSource ULTRAVISTAv20100429 Extended source Ks mag, no aperture correction applied 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 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 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 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 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 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 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 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 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 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 svNgc253Source SVNGC253v20100429 Error in extended source Ks mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
ksAperMag4Err svOrionSource SVORIONv20100429 Error in extended source Ks mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
ksAperMag4Err ultravistaSource ULTRAVISTAv20100429 Error in extended source Ks mag (2.8 arcsec aperture diameter) 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 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 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 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 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 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 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 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 vvvSource VVVDR2 Error in extended source Ks mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
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 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 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 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 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 svNgc253Source SVNGC253v20100429 Extended source Ks aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
ksAperMag6 svOrionSource SVORIONv20100429 Extended source Ks aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
ksAperMag6 ultravistaSource ULTRAVISTAv20100429 Extended source Ks mag, no aperture correction applied 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 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 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 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 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 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
ksAperMag6Err svNgc253Source SVNGC253v20100429 Error in extended source Ks mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
ksAperMag6Err svOrionSource SVORIONv20100429 Error in extended source Ks mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
ksAperMag6Err ultravistaSource ULTRAVISTAv20100429 Error in extended source Ks mag (5.7 arcsec aperture diameter) 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 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 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 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 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 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
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 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 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 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 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 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
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 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 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 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 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 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
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 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 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 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 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 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
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 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 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 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 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 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 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 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 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 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 svNgc253Source SVNGC253v20100429 average confidence in 2 arcsec diameter default aperture (aper3) Ks real 4   -99999999 meta.code
ksAverageConf svOrionSource SVORIONv20100429 average confidence in 2 arcsec diameter default aperture (aper3) Ks real 4   -99999999 meta.code
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 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 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 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 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, vmcSynopticSource VMCDR1 average confidence in 2 arcsec diameter default aperture (aper3) Ks real 4   -99999999 meta.code
ksAverageConf vvvSource VVVDR2 average confidence in 2 arcsec diameter default aperture (aper3) Ks real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
ksAverageConf vvvSource, vvvSynopticSource VVVDR1 average confidence in 2 arcsec diameter default aperture (aper3) Ks real 4   -99999999 stat.likelihood;em.IR.NIR
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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 svNgc253Source SVNGC253v20100429 discrete image classification flag in Ks smallint 2   -9999 src.class
ksClass svOrionSource SVORIONv20100429 discrete image classification flag in Ks smallint 2   -9999 src.class
ksClass ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 vvvSource VVVDR2 discrete image classification flag in Ks smallint 2   -9999 src.class
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 svNgc253Source SVNGC253v20100429 N(0,1) stellarness-of-profile statistic in Ks real 4   -0.9999995e9 stat
ksClassStat svOrionSource SVORIONv20100429 N(0,1) stellarness-of-profile statistic in Ks real 4   -0.9999995e9 stat
ksClassStat ultravistaSource ULTRAVISTAv20100429 S-Extractor classification statistic in Ks real 4   -0.9999995e9 stat
ksClassStat ultravistaSourceRemeasurement ULTRAVISTAv20100429 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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 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 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 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 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 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 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 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 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 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 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 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 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 ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 placeholder flag indicating parent/child relation in Ks int 4   -99999999 meta.code
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 svNgc253Source SVNGC253v20100429 1-b/a, where a/b=semi-major/minor axes in Ks real 4   -0.9999995e9 src.ellipticity
ksEll svOrionSource SVORIONv20100429 1-b/a, where a/b=semi-major/minor axes in Ks real 4   -0.9999995e9 src.ellipticity
ksEll ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 1-b/a, where a/b=semi-major/minor axes in Ks real 4   -0.9999995e9 src.ellipticity
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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 vvvSource VVVDR2 1-b/a, where a/b=semi-major/minor axes in Ks real 4   -0.9999995e9 src.ellipticity
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 svNgc253MergeLog SVNGC253v20100429 the extension number of this Ks frame tinyint 1     meta.number
kseNum svOrionMergeLog SVORIONv20100429 the extension number of this Ks frame tinyint 1     meta.number
kseNum ultravistaMergeLog ULTRAVISTAv20100429 the extension number of this Ks frame tinyint 1     meta.number
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 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 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 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 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 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, vmcSynopticMergeLog VMCDR1 the extension number of this Ks frame tinyint 1     meta.number
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, vvvSynopticMergeLog VVVDR1 the extension number of this Ks frame tinyint 1     meta.number
ksErrBits svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 ultravistaSourceRemeasurement ULTRAVISTAv20100429 processing warning/error bitwise flags in Ks int 4   -99999999 meta.code
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 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 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 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 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 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 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, 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 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 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 svNgc253Source SVNGC253v20100429 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 svOrionSource SVORIONv20100429 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 ultravistaSource ULTRAVISTAv20100429 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 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 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 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 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 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 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, 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 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 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 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 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 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 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 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 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 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 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 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 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 svNgc253Source SVNGC253v20100429 RMS of axes of ellipse fit in Ks real 4 pixels -0.9999995e9 src.morph.param
ksGausig svOrionSource SVORIONv20100429 RMS of axes of ellipse fit in Ks real 4 pixels -0.9999995e9 src.morph.param
ksGausig ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 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 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, 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 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, vikingSourceRemeasurement VIKINGv20110714 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 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, 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 vvvSource VVVDR2 RMS of axes of ellipse fit in Ks real 4 pixels -0.9999995e9 src.morph.param
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 videoSource VIDEODR4 SExtractor half-light radius in Ks band real 4 pixels -0.9999995e9 phys.angSize;em.IR.K
ksHlCorSMjRadAs svNgc253Source SVNGC253v20100429 Seeing corrected half-light, semi-major axis in Ks band real 4 arcsec -0.9999995e9 phys.angSize;src
ksHlCorSMjRadAs ultravistaSource ULTRAVISTAv20100429 Seeing corrected half-light, semi-major axis in Ks band real 4 arcsec -0.9999995e9 phys.angSize;src
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 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 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 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
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 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 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 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 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 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 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 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 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 VVVv20110718 Use a default model for the astrometric noise in Ks band. tinyint 1   0  
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 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 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 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 VVVv20110718 Use a default model for the photometric noise in Ks band. tinyint 1   0  
ksKronMag videoSource VIDEODR4 Extended source Ks mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.K
ksKronMagErr videoSource VIDEODR4 Extended source Ks mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;em.IR.K;phot.mag
ksMag ultravistaSourceRemeasurement ULTRAVISTAv20100429 Ks mag (as appropriate for this merged source) real 4 mag -0.9999995e9 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 ultravistaSourceRemeasurement ULTRAVISTAv20100429 Error in Ks mag real 4 mag -0.9999995e9 stat.error
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 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 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 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 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 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 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 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 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 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 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 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 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 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
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 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 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 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 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 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
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 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 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 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 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.
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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 svNgc253MergeLog SVNGC253v20100429 the UID of the relevant Ks multiframe bigint 8     obs.field
ksmfID svOrionMergeLog SVORIONv20100429 the UID of the relevant Ks multiframe bigint 8     obs.field
ksmfID ultravistaMergeLog ULTRAVISTAv20100429 the UID of the relevant Ks multiframe bigint 8     obs.field
ksmfID vhsMergeLog VHSDR1 the UID of the relevant Ks multiframe bigint