J 
Name  Schema Table  Database  Description  Type  Length  Unit  Default Value  Unified Content Descriptor 
J 
twomass 
SIXDF 
J magnitude (JEXT) used for J selection 
real 
4 
mag 


j_2mrat 
twomass_scn 
2MASS 
Jband average 2nd image moment ratio. 
real 
4 


stat.fit.param 
j_2mrat 
twomass_sixx2_scn 
2MASS 
J band average 2nd image moment ratio for scan 
real 
4 



j_5sig_ba 
twomass_xsc 
2MASS 
J minor/major axis ratio fit to the 5sigma isophote. 
real 
4 


phys.size.axisRatio 
j_5sig_phi 
twomass_xsc 
2MASS 
J angle to 5sigma major axis (E of N). 
smallint 
2 
degrees 

stat.error 
j_5surf 
twomass_xsc 
2MASS 
J central surface brightness (r<=5). 
real 
4 
mag 

phot.mag.sb 
j_ba 
twomass_xsc 
2MASS 
J minor/major axis ratio fit to the 3sigma isophote. 
real 
4 


phys.size.axisRatio 
j_back 
twomass_xsc 
2MASS 
J coadd median background. 
real 
4 


meta.code 
j_bisym_chi 
twomass_xsc 
2MASS 
J bisymmetric crosscorrelation chi. 
real 
4 


stat.fit.param 
j_bisym_rat 
twomass_xsc 
2MASS 
J bisymmetric flux ratio. 
real 
4 


phot.flux;arith.ratio 
j_bndg_amp 
twomass_xsc 
2MASS 
J banding maximum FT amplitude on this side of coadd. 
real 
4 
DN 

stat.fit.param 
j_bndg_per 
twomass_xsc 
2MASS 
J banding Fourier Transf. period on this side of coadd. 
int 
4 
arcsec 

stat.fit.param 
j_chif_ellf 
twomass_xsc 
2MASS 
J % chifraction for elliptical fit to 3sig isophote. 
real 
4 


stat.fit.param 
j_cmsig 
twomass_psc 
2MASS 
Corrected photometric uncertainty for the default Jband magnitude. 
real 
4 
mag 
Jband 
phot.flux 
j_con_indx 
twomass_xsc 
2MASS 
J concentration index r_75%/r_25%. 
real 
4 


phys.size;arith.ratio 
j_d_area 
twomass_xsc 
2MASS 
J 5sigma to 3sigma differential area. 
smallint 
2 


stat.fit.residual 
j_flg_10 
twomass_xsc 
2MASS 
J confusion flag for 10 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_15 
twomass_xsc 
2MASS 
J confusion flag for 15 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_20 
twomass_xsc 
2MASS 
J confusion flag for 20 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_25 
twomass_xsc 
2MASS 
J confusion flag for 25 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_30 
twomass_xsc 
2MASS 
J confusion flag for 30 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_40 
twomass_xsc 
2MASS 
J confusion flag for 40 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_5 
twomass_xsc 
2MASS 
J confusion flag for 5 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_50 
twomass_xsc 
2MASS 
J confusion flag for 50 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_60 
twomass_xsc 
2MASS 
J confusion flag for 60 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_7 
twomass_sixx2_xsc 
2MASS 
J confusion flag for 7 arcsec circular ap. mag 
smallint 
2 



j_flg_7 
twomass_xsc 
2MASS 
J confusion flag for 7 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_70 
twomass_xsc 
2MASS 
J confusion flag for 70 arcsec circular ap. mag. 
smallint 
2 


meta.code 
j_flg_c 
twomass_xsc 
2MASS 
J confusion flag for Kron circular mag. 
smallint 
2 


meta.code 
j_flg_e 
twomass_xsc 
2MASS 
J confusion flag for Kron elliptical mag. 
smallint 
2 


meta.code 
j_flg_fc 
twomass_xsc 
2MASS 
J confusion flag for fiducial Kron circ. mag. 
smallint 
2 


meta.code 
j_flg_fe 
twomass_xsc 
2MASS 
J confusion flag for fiducial Kron ell. mag. 
smallint 
2 


meta.code 
j_flg_i20c 
twomass_xsc 
2MASS 
J confusion flag for 20mag/sq." iso. circ. mag. 
smallint 
2 


meta.code 
j_flg_i20e 
twomass_xsc 
2MASS 
J confusion flag for 20mag/sq." iso. ell. mag. 
smallint 
2 


meta.code 
j_flg_i21c 
twomass_xsc 
2MASS 
J confusion flag for 21mag/sq." iso. circ. mag. 
smallint 
2 


meta.code 
j_flg_i21e 
twomass_xsc 
2MASS 
J confusion flag for 21mag/sq." iso. ell. mag. 
smallint 
2 


meta.code 
j_flg_j21fc 
twomass_xsc 
2MASS 
J confusion flag for 21mag/sq." iso. fid. circ. mag. 
smallint 
2 


meta.code 
j_flg_j21fe 
twomass_xsc 
2MASS 
J confusion flag for 21mag/sq." iso. fid. ell. mag. 
smallint 
2 


meta.code 
j_flg_k20fc 
twomass_xsc 
2MASS 
J confusion flag for 20mag/sq." iso. fid. circ. mag. 
smallint 
2 


meta.code 
j_flg_k20fe 
twomass_sixx2_xsc 
2MASS 
J confusion flag for 20mag/sq.″ iso. fid. ell. mag 
smallint 
2 



j_flg_k20fe 
twomass_xsc 
2MASS 
J confusion flag for 20mag/sq." iso. fid. ell. mag. 
smallint 
2 


meta.code 
j_h 
twomass_sixx2_psc 
2MASS 
The JH color, computed from the Jband and Hband magnitudes (j_m and h_m, respectively) of the source. In cases where the first or second digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected. 
real 
4 



j_k 
twomass_sixx2_psc 
2MASS 
The JKs color, computed from the Jband and Ksband magnitudes (j_m and k_m, respectively) of the source. In cases where the first or third digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected. 
real 
4 



j_m 
twomass_psc 
2MASS 
Default Jband magnitude 
real 
4 
mag 

phot.flux 
j_m 
twomass_sixx2_psc 
2MASS 
J selected "default" magnitude 
real 
4 
mag 


j_m_10 
twomass_xsc 
2MASS 
J 10 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_15 
twomass_xsc 
2MASS 
J 15 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_20 
twomass_xsc 
2MASS 
J 20 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_25 
twomass_xsc 
2MASS 
J 25 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_2mass 
allwise_sc 
WISE 
2MASS Jband magnitude or magnitude upper limit of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC Jband magnitude entry is "null". 
float 
8 
mag 


j_m_2mass 
wise_allskysc 
WISE 
2MASS Jband 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 Jband magnitude entry is default. 
real 
4 
mag 
0.9999995e9 

j_m_2mass 
wise_prelimsc 
WISE 
2MASS Jband 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 Jband magnitude entry is default 
real 
4 
mag 
0.9999995e9 

j_m_30 
twomass_xsc 
2MASS 
J 30 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_40 
twomass_xsc 
2MASS 
J 40 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_5 
twomass_xsc 
2MASS 
J 5 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_50 
twomass_xsc 
2MASS 
J 50 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_60 
twomass_xsc 
2MASS 
J 60 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_7 
twomass_sixx2_xsc 
2MASS 
J 7 arcsec radius circular aperture magnitude 
real 
4 
mag 


j_m_7 
twomass_xsc 
2MASS 
J 7 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_70 
twomass_xsc 
2MASS 
J 70 arcsec radius circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_c 
twomass_xsc 
2MASS 
J Kron circular aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_e 
twomass_xsc 
2MASS 
J Kron elliptical aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_ext 
twomass_sixx2_xsc 
2MASS 
J mag from fit extrapolation 
real 
4 
mag 


j_m_ext 
twomass_xsc 
2MASS 
J mag from fit extrapolation. 
real 
4 
mag 

phot.flux 
j_m_fc 
twomass_xsc 
2MASS 
J fiducial Kron circular magnitude. 
real 
4 
mag 

phot.flux 
j_m_fe 
twomass_xsc 
2MASS 
J fiducial Kron ell. mag aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_i20c 
twomass_xsc 
2MASS 
J 20mag/sq." isophotal circular ap. magnitude. 
real 
4 
mag 

phot.flux 
j_m_i20e 
twomass_xsc 
2MASS 
J 20mag/sq." isophotal elliptical ap. magnitude. 
real 
4 
mag 

phot.flux 
j_m_i21c 
twomass_xsc 
2MASS 
J 21mag/sq." isophotal circular ap. magnitude. 
real 
4 
mag 

phot.flux 
j_m_i21e 
twomass_xsc 
2MASS 
J 21mag/sq." isophotal elliptical ap. magnitude. 
real 
4 
mag 

phot.flux 
j_m_j21fc 
twomass_xsc 
2MASS 
J 21mag/sq." isophotal fiducial circ. ap. mag. 
real 
4 
mag 

phot.flux 
j_m_j21fe 
twomass_xsc 
2MASS 
J 21mag/sq." isophotal fiducial ell. ap. magnitude. 
real 
4 
mag 

phot.flux 
j_m_k20fc 
twomass_xsc 
2MASS 
J 20mag/sq." isophotal fiducial circ. ap. mag. 
real 
4 
mag 

phot.flux 
J_M_K20FE 
twomass 
SIXDF 
J 20mag/sq." isophotal fiducial ell. ap. magnitude 
real 
4 
mag 


j_m_k20fe 
twomass_sixx2_xsc 
2MASS 
J 20mag/sq.″ isophotal fiducial ell. ap. magnitude 
real 
4 
mag 


j_m_k20fe 
twomass_xsc 
2MASS 
J 20mag/sq." isophotal fiducial ell. ap. magnitude. 
real 
4 
mag 

phot.flux 
j_m_stdap 
twomass_psc 
2MASS 
Jband "standard" aperture magnitude. 
real 
4 
mag 

phot.flux 
j_m_sys 
twomass_xsc 
2MASS 
J system photometry magnitude. 
real 
4 
mag 

phot.flux 
j_mnsurfb_eff 
twomass_xsc 
2MASS 
J mean surface brightness at the halflight radius. 
real 
4 
mag 

phot.mag.sb 
j_msig 
twomass_sixx2_psc 
2MASS 
J "default" mag uncertainty 
real 
4 
mag 


j_msig_10 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 10 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_15 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 15 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_20 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 20 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_25 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 25 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_2mass 
allwise_sc 
WISE 
2MASS Jband 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 Jband uncertainty entry is "null". 
float 
8 
mag 


j_msig_2mass 
wise_allskysc 
WISE 
2MASS Jband 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 Jband uncertainty entry is default. 
real 
4 
mag 
0.9999995e9 

j_msig_2mass 
wise_prelimsc 
WISE 
2MASS Jband 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 Jband uncertainty entry is default 
real 
4 
mag 
0.9999995e9 

j_msig_30 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 30 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_40 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 40 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_5 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 5 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_50 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 50 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_60 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 60 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_7 
twomass_sixx2_xsc 
2MASS 
J 1sigma uncertainty in 7 arcsec circular ap. mag 
real 
4 
mag 


j_msig_7 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 7 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_70 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 70 arcsec circular ap. mag. 
real 
4 
mag 

stat.error 
j_msig_c 
twomass_xsc 
2MASS 
J 1sigma uncertainty in Kron circular mag. 
real 
4 
mag 

stat.error 
j_msig_e 
twomass_xsc 
2MASS 
J 1sigma uncertainty in Kron elliptical mag. 
real 
4 
mag 

stat.error 
j_msig_ext 
twomass_sixx2_xsc 
2MASS 
J 1sigma uncertainty in mag from fit extrapolation 
real 
4 
mag 


j_msig_ext 
twomass_xsc 
2MASS 
J 1sigma uncertainty in mag from fit extrapolation. 
real 
4 
mag 

stat.error 
j_msig_fc 
twomass_xsc 
2MASS 
J 1sigma uncertainty in fiducial Kron circ. mag. 
real 
4 
mag 

stat.error 
j_msig_fe 
twomass_xsc 
2MASS 
J 1sigma uncertainty in fiducial Kron ell. mag. 
real 
4 
mag 

stat.error 
j_msig_i20c 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 20mag/sq." iso. circ. mag. 
real 
4 
mag 

stat.error 
j_msig_i20e 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 20mag/sq." iso. ell. mag. 
real 
4 
mag 

stat.error 
j_msig_i21c 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 21mag/sq." iso. circ. mag. 
real 
4 
mag 

stat.error 
j_msig_i21e 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 21mag/sq." iso. ell. mag. 
real 
4 
mag 

stat.error 
j_msig_j21fc 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 21mag/sq." iso.fid.circ.mag. 
real 
4 
mag 

stat.error 
j_msig_j21fe 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 21mag/sq." iso.fid.ell.mag. 
real 
4 
mag 

stat.error 
j_msig_k20fc 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 20mag/sq." iso.fid.circ. mag. 
real 
4 
mag 

stat.error 
j_msig_k20fe 
twomass_xsc 
2MASS 
J 1sigma uncertainty in 20mag/sq." iso.fid.ell.mag. 
real 
4 
mag 

stat.error 
j_msig_stdap 
twomass_psc 
2MASS 
Uncertainty in the Jband standard aperture magnitude. 
real 
4 
mag 

phot.flux 
j_msig_sys 
twomass_xsc 
2MASS 
J 1sigma uncertainty in system photometry mag. 
real 
4 
mag 

stat.error 
j_msigcom 
twomass_psc 
2MASS 
Combined, or total photometric uncertainty for the default Jband magnitude. 
real 
4 
mag 
Jband 
phot.flux 
j_msigcom 
twomass_sixx2_psc 
2MASS 
combined (total) J band photometric uncertainty 
real 
4 
mag 


j_msnr10 
twomass_scn 
2MASS 
The estimated Jband magnitude at which SNR=10 is achieved for this scan. 
real 
4 
mag 

phot.flux 
j_msnr10 
twomass_sixx2_scn 
2MASS 
J mag at which SNR=10 is achieved, from j_psp and j_zp_ap 
real 
4 
mag 


j_n_snr10 
twomass_scn 
2MASS 
Number of point sources at Jband with SNR>10 (instrumental mag <=15.8) 
int 
4 


meta.number 
j_n_snr10 
twomass_sixx2_scn 
2MASS 
number of J point sources with SNR>10 (instrumental m<=15.8) 
int 
4 



j_pchi 
twomass_xsc 
2MASS 
J chi^2 of fit to rad. profile (LCSB: alpha scale len). 
real 
4 


stat.fit.param 
j_peak 
twomass_xsc 
2MASS 
J peak pixel brightness. 
real 
4 
mag 

phot.mag.sb 
j_perc_darea 
twomass_xsc 
2MASS 
J 5sigma to 3sigma percent area change. 
smallint 
2 


FIT_PARAM 
j_phi 
twomass_xsc 
2MASS 
J angle to 3sigma major axis (E of N). 
smallint 
2 
degrees 

pos.posAng 
j_psfchi 
twomass_psc 
2MASS 
Reduced chisquared goodnessoffit value for the Jband profilefit photometry made on the 1.3 s "Read_2" exposures. 
real 
4 


stat.fit.param 
j_psp 
twomass_scn 
2MASS 
Jband photometric sensitivity paramater (PSP). 
real 
4 


instr.sensitivity 
j_psp 
twomass_sixx2_scn 
2MASS 
J photometric sensitivity param: j_shape_avg*(j_fbg_avg^.29) 
real 
4 



j_pts_noise 
twomass_scn 
2MASS 
Base10 logarithm of the mode of the noise distribution for all point source detections in the scan, where the noise is estimated from the measured Jband photometric errors and is expressed in units of mJy. 
real 
4 


instr.det.noise 
j_pts_noise 
twomass_sixx2_scn 
2MASS 
log10 of J band modal point src noise estimate 
real 
4 
logmJy 


j_r_c 
twomass_xsc 
2MASS 
J Kron circular aperture radius. 
real 
4 
arcsec 

phys.angSize;src 
j_r_e 
twomass_xsc 
2MASS 
J Kron elliptical aperture semimajor axis. 
real 
4 
arcsec 

phys.angSize;src 
j_r_eff 
twomass_xsc 
2MASS 
J halflight (integrated halfflux point) radius. 
real 
4 
arcsec 

phys.angSize;src 
j_r_i20c 
twomass_xsc 
2MASS 
J 20mag/sq." isophotal circular aperture radius. 
real 
4 
arcsec 

phys.angSize;src 
j_r_i20e 
twomass_xsc 
2MASS 
J 20mag/sq." isophotal elliptical ap. semimajor axis. 
real 
4 
arcsec 

phys.angSize;src 
j_r_i21c 
twomass_xsc 
2MASS 
J 21mag/sq." isophotal circular aperture radius. 
real 
4 
arcsec 

phys.angSize;src 
j_r_i21e 
twomass_xsc 
2MASS 
J 21mag/sq." isophotal elliptical ap. semimajor axis. 
real 
4 
arcsec 

phys.angSize;src 
j_resid_ann 
twomass_xsc 
2MASS 
J residual annulus background median. 
real 
4 
DN 

meta.code 
j_sc_1mm 
twomass_xsc 
2MASS 
J 1st moment (score) (LCSB: super blk 2,4,8 SNR). 
real 
4 


meta.code 
j_sc_2mm 
twomass_xsc 
2MASS 
J 2nd moment (score) (LCSB: SNRMAX  super SNR max). 
real 
4 


meta.code 
j_sc_msh 
twomass_xsc 
2MASS 
J median shape score. 
real 
4 


meta.code 
j_sc_mxdn 
twomass_xsc 
2MASS 
J mxdn (score) (LCSB: BSNR  block/smoothed SNR). 
real 
4 


meta.code 
j_sc_r1 
twomass_xsc 
2MASS 
J r1 (score). 
real 
4 


meta.code 
j_sc_r23 
twomass_xsc 
2MASS 
J r23 (score) (LCSB: TSNR  integrated SNR for r=15). 
real 
4 


meta.code 
j_sc_sh 
twomass_xsc 
2MASS 
J shape (score). 
real 
4 


meta.code 
j_sc_vint 
twomass_xsc 
2MASS 
J vint (score). 
real 
4 


meta.code 
j_sc_wsh 
twomass_xsc 
2MASS 
J wsh (score) (LCSB: PSNR  peak raw SNR). 
real 
4 


meta.code 
j_seetrack 
twomass_xsc 
2MASS 
J band seetracking score. 
real 
4 


meta.code 
j_sh0 
twomass_xsc 
2MASS 
J ridge shape (LCSB: BSNR limit). 
real 
4 


FIT_PARAM 
j_shape_avg 
twomass_scn 
2MASS 
Jband average seeing shape for scan. 
real 
4 


instr.obsty.seeing 
j_shape_avg 
twomass_sixx2_scn 
2MASS 
J band average seeing shape for scan 
real 
4 



j_shape_rms 
twomass_scn 
2MASS 
RMSerror of Jband average seeing shape. 
real 
4 


instr.obsty.seeing 
j_shape_rms 
twomass_sixx2_scn 
2MASS 
rms of J band avg seeing shape for scan 
real 
4 



j_sig_sh0 
twomass_xsc 
2MASS 
J ridge shape sigma (LCSB: B2SNR limit). 
real 
4 


FIT_PARAM 
j_snr 
twomass_psc 
2MASS 
Jband "scan" signaltonoise ratio. 
real 
4 
mag 

instr.det.noise 
j_snr 
twomass_sixx2_psc 
2MASS 
J band "scan" signaltonoise ratio 
real 
4 



j_subst2 
twomass_xsc 
2MASS 
J residual background #2 (score). 
real 
4 


meta.code 
j_zp_ap 
twomass_scn 
2MASS 
Photometric zeropoint for Jband aperture photometry. 
real 
4 
mag 

phot.mag;arith.zp 
j_zp_ap 
twomass_sixx2_scn 
2MASS 
J band ap. calibration photometric zeropoint for scan 
real 
4 
mag 


jAperMag1 
vmcSynopticSource 
VMCDR1 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vmcSynopticSource 
VMCDR2 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1 
vmcSynopticSource 
VMCDR3 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1 
vmcSynopticSource 
VMCv20110816 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vmcSynopticSource 
VMCv20110909 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vmcSynopticSource 
VMCv20120126 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vmcSynopticSource 
VMCv20121128 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vmcSynopticSource 
VMCv20130304 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vmcSynopticSource 
VMCv20130805 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1 
vmcSynopticSource 
VMCv20140428 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1 
vmcSynopticSource 
VMCv20140903 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1 
vmcSynopticSource 
VMCv20150309 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1 
vvvSource 
VVVDR1 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vvvSource 
VVVv20100531 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vvvSource 
VVVv20110718 
Extended source J aperture corrected mag (0.7 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag1 
vvvSource, vvvSynopticSource 
VVVDR2 
Extended source J aperture corrected mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag1Err 
vmcSynopticSource 
VMCDR1 
Error in extended source J mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCDR2 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCDR3 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag1Err 
vmcSynopticSource 
VMCv20110816 
Error in extended source J mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCv20110909 
Error in extended source J mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCv20120126 
Error in extended source J mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCv20121128 
Error in extended source J mag (0.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCv20130304 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCv20130805 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vmcSynopticSource 
VMCv20140428 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag1Err 
vmcSynopticSource 
VMCv20140903 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag1Err 
vmcSynopticSource 
VMCv20150309 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag1Err 
vvvSource 
VVVDR1 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vvvSource 
VVVv20100531 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vvvSource 
VVVv20110718 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag1Err 
vvvSource, vvvSynopticSource 
VVVDR2 
Error in extended source J mag (1.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2 
vmcSynopticSource 
VMCDR1 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vmcSynopticSource 
VMCDR2 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2 
vmcSynopticSource 
VMCDR3 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2 
vmcSynopticSource 
VMCv20110816 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vmcSynopticSource 
VMCv20110909 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vmcSynopticSource 
VMCv20120126 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vmcSynopticSource 
VMCv20121128 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vmcSynopticSource 
VMCv20130304 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vmcSynopticSource 
VMCv20130805 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2 
vmcSynopticSource 
VMCv20140428 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2 
vmcSynopticSource 
VMCv20140903 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2 
vmcSynopticSource 
VMCv20150309 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2 
vvvSynopticSource 
VVVDR1 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag2 
vvvSynopticSource 
VVVDR2 
Extended source J aperture corrected mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag2Err 
vmcSynopticSource 
VMCDR1 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCDR2 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCDR3 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag2Err 
vmcSynopticSource 
VMCv20110816 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCv20110909 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCv20120126 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCv20121128 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCv20130304 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCv20130805 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vmcSynopticSource 
VMCv20140428 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag2Err 
vmcSynopticSource 
VMCv20140903 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag2Err 
vmcSynopticSource 
VMCv20150309 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag2Err 
vvvSynopticSource 
VVVDR1 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag2Err 
vvvSynopticSource 
VVVDR2 
Error in extended source J mag (1.4 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3 
svNgc253Source 
SVNGC253v20100429 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
svOrionSource 
SVORIONv20100429 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
ultravistaSource 
ULTRAVISTAv20100429 
Default point/extended source J mag, no aperture correction applied If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vhsSource 
VHSDR1 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vhsSource 
VHSDR2 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vhsSource 
VHSDR3 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vhsSource 
VHSv20120926 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vhsSource 
VHSv20130417 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vhsSource 
VHSv20140409 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vhsSource 
VHSv20150108 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
videoSource 
VIDEODR2 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
videoSource 
VIDEODR3 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
videoSource 
VIDEODR4 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
videoSource 
VIDEOv20100513 
Default point/extended source J mag, no aperture correction applied If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
videoSource 
VIDEOv20111208 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vikingSource 
VIKINGDR2 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vikingSource 
VIKINGDR3 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vikingSource 
VIKINGDR4 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vikingSource 
VIKINGv20110714 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vikingSource 
VIKINGv20111019 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vikingSource 
VIKINGv20130417 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vikingSource 
VIKINGv20140402 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vikingSource 
VIKINGv20150421 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSource 
VMCDR1 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSource 
VMCDR2 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSource 
VMCDR3 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSource 
VMCv20110816 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSource 
VMCv20110909 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSource 
VMCv20120126 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSource 
VMCv20121128 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSource 
VMCv20130304 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSource 
VMCv20130805 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSource 
VMCv20140428 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSource 
VMCv20140903 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSource 
VMCv20150309 
Default point source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSynopticSource 
VMCDR1 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSynopticSource 
VMCDR2 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSynopticSource 
VMCDR3 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSynopticSource 
VMCv20110816 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSynopticSource 
VMCv20110909 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSynopticSource 
VMCv20120126 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSynopticSource 
VMCv20121128 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSynopticSource 
VMCv20130304 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vmcSynopticSource 
VMCv20130805 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSynopticSource 
VMCv20140428 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSynopticSource 
VMCv20140903 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vmcSynopticSource 
VMCv20150309 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vvvSource 
VVVDR1 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vvvSource 
VVVDR2 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3 
vvvSource 
VVVv20100531 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vvvSource 
VVVv20110718 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vvvSynopticSource 
VVVDR1 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag3 
vvvSynopticSource 
VVVDR2 
Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag3Err 
svNgc253Source 
SVNGC253v20100429 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
svOrionSource 
SVORIONv20100429 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
ultravistaSource 
ULTRAVISTAv20100429 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vhsSource 
VHSDR1 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vhsSource 
VHSDR2 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vhsSource 
VHSDR3 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag3Err 
vhsSource 
VHSv20120926 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vhsSource 
VHSv20130417 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vhsSource 
VHSv20140409 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag3Err 
vhsSource 
VHSv20150108 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag3Err 
videoSource 
VIDEODR2 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
videoSource 
VIDEODR3 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
videoSource 
VIDEODR4 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag3Err 
videoSource 
VIDEOv20100513 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
videoSource 
VIDEOv20111208 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGDR2 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGDR3 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGDR4 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag3Err 
vikingSource 
VIKINGv20110714 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGv20111019 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGv20130417 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGv20140402 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vikingSource 
VIKINGv20150421 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag3Err 
vmcSource 
VMCDR2 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCDR3 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag3Err 
vmcSource 
VMCv20110816 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCv20110909 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCv20120126 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCv20121128 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCv20130304 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCv20130805 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vmcSource 
VMCv20140428 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag3Err 
vmcSource 
VMCv20140903 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag3Err 
vmcSource 
VMCv20150309 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag3Err 
vmcSource, vmcSynopticSource 
VMCDR1 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vvvSource 
VVVDR2 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vvvSource 
VVVv20100531 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vvvSource 
VVVv20110718 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag3Err 
vvvSource, vvvSynopticSource 
VVVDR1 
Error in default point/extended source J mag (2.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4 
svNgc253Source 
SVNGC253v20100429 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
svOrionSource 
SVORIONv20100429 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
ultravistaSource 
ULTRAVISTAv20100429 
Extended source J mag, no aperture correction applied 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vhsSource 
VHSDR1 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vhsSource 
VHSDR2 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vhsSource 
VHSDR3 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vhsSource 
VHSv20120926 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vhsSource 
VHSv20130417 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vhsSource 
VHSv20140409 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vhsSource 
VHSv20150108 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
videoSource 
VIDEODR2 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
videoSource 
VIDEODR3 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
videoSource 
VIDEODR4 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
videoSource 
VIDEOv20100513 
Extended source J mag, no aperture correction applied 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
videoSource 
VIDEOv20111208 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vikingSource 
VIKINGDR2 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vikingSource 
VIKINGDR3 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vikingSource 
VIKINGDR4 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vikingSource 
VIKINGv20110714 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vikingSource 
VIKINGv20111019 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vikingSource 
VIKINGv20130417 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vikingSource 
VIKINGv20140402 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vikingSource 
VIKINGv20150421 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSource 
VMCDR1 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSource 
VMCDR2 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSource 
VMCDR3 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSource 
VMCv20110816 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSource 
VMCv20110909 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSource 
VMCv20120126 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSource 
VMCv20121128 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSource 
VMCv20130304 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSource 
VMCv20130805 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSource 
VMCv20140428 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSource 
VMCv20140903 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSource 
VMCv20150309 
Point source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSynopticSource 
VMCDR1 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSynopticSource 
VMCDR2 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSynopticSource 
VMCDR3 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSynopticSource 
VMCv20110816 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSynopticSource 
VMCv20110909 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSynopticSource 
VMCv20120126 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSynopticSource 
VMCv20121128 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSynopticSource 
VMCv20130304 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vmcSynopticSource 
VMCv20130805 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSynopticSource 
VMCv20140428 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSynopticSource 
VMCv20140903 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vmcSynopticSource 
VMCv20150309 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vvvSource 
VVVDR2 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag4 
vvvSource 
VVVv20100531 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vvvSource 
VVVv20110718 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4 
vvvSource, vvvSynopticSource 
VVVDR1 
Extended source J aperture corrected mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag4Err 
svNgc253Source 
SVNGC253v20100429 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
svOrionSource 
SVORIONv20100429 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
ultravistaSource 
ULTRAVISTAv20100429 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vhsSource 
VHSDR1 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vhsSource 
VHSDR2 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vhsSource 
VHSDR3 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag4Err 
vhsSource 
VHSv20120926 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vhsSource 
VHSv20130417 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vhsSource 
VHSv20140409 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag4Err 
vhsSource 
VHSv20150108 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
videoSource 
VIDEODR2 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
videoSource 
VIDEODR3 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
videoSource 
VIDEODR4 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
videoSource 
VIDEOv20100513 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
videoSource 
VIDEOv20111208 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGDR2 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGDR3 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGDR4 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag4Err 
vikingSource 
VIKINGv20110714 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGv20111019 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGv20130417 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGv20140402 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vikingSource 
VIKINGv20150421 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vmcSource 
VMCDR1 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCDR2 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCDR3 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vmcSource 
VMCv20110816 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCv20110909 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCv20120126 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCv20121128 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCv20130304 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCv20130805 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSource 
VMCv20140428 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag4Err 
vmcSource 
VMCv20140903 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vmcSource 
VMCv20150309 
Error in point/extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vmcSynopticSource 
VMCDR1 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCDR2 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCDR3 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vmcSynopticSource 
VMCv20110816 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCv20110909 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCv20120126 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCv20121128 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCv20130304 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCv20130805 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vmcSynopticSource 
VMCv20140428 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag4Err 
vmcSynopticSource 
VMCv20140903 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vmcSynopticSource 
VMCv20150309 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag4Err 
vvvSource 
VVVDR2 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vvvSource 
VVVv20100531 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vvvSource 
VVVv20110718 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag4Err 
vvvSource, vvvSynopticSource 
VVVDR1 
Error in extended source J mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5 
vmcSynopticSource 
VMCDR1 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vmcSynopticSource 
VMCDR2 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5 
vmcSynopticSource 
VMCDR3 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5 
vmcSynopticSource 
VMCv20110816 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vmcSynopticSource 
VMCv20110909 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vmcSynopticSource 
VMCv20120126 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vmcSynopticSource 
VMCv20121128 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vmcSynopticSource 
VMCv20130304 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vmcSynopticSource 
VMCv20130805 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5 
vmcSynopticSource 
VMCv20140428 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5 
vmcSynopticSource 
VMCv20140903 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5 
vmcSynopticSource 
VMCv20150309 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5 
vvvSynopticSource 
VVVDR1 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag5 
vvvSynopticSource 
VVVDR2 
Extended source J aperture corrected mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag5Err 
vmcSynopticSource 
VMCDR1 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCDR2 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCDR3 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag5Err 
vmcSynopticSource 
VMCv20110816 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCv20110909 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCv20120126 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCv20121128 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCv20130304 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCv20130805 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vmcSynopticSource 
VMCv20140428 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag5Err 
vmcSynopticSource 
VMCv20140903 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag5Err 
vmcSynopticSource 
VMCv20150309 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag5Err 
vvvSynopticSource 
VVVDR1 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag5Err 
vvvSynopticSource 
VVVDR2 
Error in extended source J mag (4.0 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6 
svNgc253Source 
SVNGC253v20100429 
Extended source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
svOrionSource 
SVORIONv20100429 
Extended source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
ultravistaSource 
ULTRAVISTAv20100429 
Extended source J mag, no aperture correction applied 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vhsSource 
VHSDR1 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vhsSource 
VHSDR2 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vhsSource 
VHSDR3 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vhsSource 
VHSv20120926 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vhsSource 
VHSv20130417 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vhsSource 
VHSv20140409 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vhsSource 
VHSv20150108 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
videoSource 
VIDEODR2 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
videoSource 
VIDEODR3 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
videoSource 
VIDEODR4 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
videoSource 
VIDEOv20100513 
Extended source J mag, no aperture correction applied 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
videoSource 
VIDEOv20111208 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vikingSource 
VIKINGDR2 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vikingSource 
VIKINGDR3 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vikingSource 
VIKINGDR4 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vikingSource 
VIKINGv20110714 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vikingSource 
VIKINGv20111019 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vikingSource 
VIKINGv20130417 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vikingSource 
VIKINGv20140402 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vikingSource 
VIKINGv20150421 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vmcSource 
VMCDR1 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vmcSource 
VMCDR2 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vmcSource 
VMCDR3 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vmcSource 
VMCv20110816 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vmcSource 
VMCv20110909 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vmcSource 
VMCv20120126 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vmcSource 
VMCv20121128 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vmcSource 
VMCv20130304 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMag6 
vmcSource 
VMCv20130805 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vmcSource 
VMCv20140428 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vmcSource 
VMCv20140903 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6 
vmcSource 
VMCv20150309 
Point source J aperture corrected mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMag6Err 
svNgc253Source 
SVNGC253v20100429 
Error in extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
svOrionSource 
SVORIONv20100429 
Error in extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
ultravistaSource 
ULTRAVISTAv20100429 
Error in extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vhsSource 
VHSDR1 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vhsSource 
VHSDR2 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vhsSource 
VHSDR3 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag6Err 
vhsSource 
VHSv20120926 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vhsSource 
VHSv20130417 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vhsSource 
VHSv20140409 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag6Err 
vhsSource 
VHSv20150108 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag6Err 
videoSource 
VIDEODR2 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
videoSource 
VIDEODR3 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
videoSource 
VIDEODR4 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag6Err 
videoSource 
VIDEOv20100513 
Error in extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
videoSource 
VIDEOv20111208 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGDR2 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGDR3 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGDR4 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag6Err 
vikingSource 
VIKINGv20110714 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGv20111019 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGv20130417 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGv20140402 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vikingSource 
VIKINGv20150421 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag6Err 
vmcSource 
VMCDR1 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCDR2 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCDR3 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag6Err 
vmcSource 
VMCv20110816 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCv20110909 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCv20120126 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCv20121128 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCv20130304 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCv20130805 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error 
jAperMag6Err 
vmcSource 
VMCv20140428 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
jAperMag6Err 
vmcSource 
VMCv20140903 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMag6Err 
vmcSource 
VMCv20150309 
Error in point/extended source J mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jAperMagNoAperCorr3 
vhsSource 
VHSDR1 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vhsSource 
VHSDR2 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vhsSource 
VHSDR3 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vhsSource 
VHSv20120926 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vhsSource 
VHSv20130417 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vhsSource 
VHSv20140409 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vhsSource 
VHSv20150108 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
videoSource 
VIDEODR2 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
videoSource 
VIDEODR3 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
videoSource 
VIDEODR4 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
videoSource 
VIDEOv20111208 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vikingSource 
VIKINGDR2 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vikingSource 
VIKINGDR3 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vikingSource 
VIKINGDR4 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vikingSource 
VIKINGv20110714 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vikingSource 
VIKINGv20111019 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vikingSource 
VIKINGv20130417 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vikingSource 
VIKINGv20140402 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vikingSource 
VIKINGv20150421 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vmcSource 
VMCDR1 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vmcSource 
VMCDR2 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vmcSource 
VMCDR3 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vmcSource 
VMCv20110816 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vmcSource 
VMCv20110909 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vmcSource 
VMCv20120126 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vmcSource 
VMCv20121128 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vmcSource 
VMCv20130304 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr3 
vmcSource 
VMCv20130805 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vmcSource 
VMCv20140428 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vmcSource 
VMCv20140903 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr3 
vmcSource 
VMCv20150309 
Default extended source J aperture mag (2.0 arcsec aperture diameter) If in doubt use this flux estimator 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vhsSource 
VHSDR1 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vhsSource 
VHSDR2 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vhsSource 
VHSDR3 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vhsSource 
VHSv20120926 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vhsSource 
VHSv20130417 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vhsSource 
VHSv20140409 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vhsSource 
VHSv20150108 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
videoSource 
VIDEODR2 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
videoSource 
VIDEODR3 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
videoSource 
VIDEODR4 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
videoSource 
VIDEOv20111208 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vikingSource 
VIKINGDR2 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vikingSource 
VIKINGDR3 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vikingSource 
VIKINGDR4 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vikingSource 
VIKINGv20110714 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vikingSource 
VIKINGv20111019 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vikingSource 
VIKINGv20130417 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vikingSource 
VIKINGv20140402 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vikingSource 
VIKINGv20150421 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vmcSource 
VMCDR1 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vmcSource 
VMCDR2 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vmcSource 
VMCDR3 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vmcSource 
VMCv20110816 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vmcSource 
VMCv20110909 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vmcSource 
VMCv20120126 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vmcSource 
VMCv20121128 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vmcSource 
VMCv20130304 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr4 
vmcSource 
VMCv20130805 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vmcSource 
VMCv20140428 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vmcSource 
VMCv20140903 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr4 
vmcSource 
VMCv20150309 
Extended source J aperture mag (2.8 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vhsSource 
VHSDR1 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vhsSource 
VHSDR2 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vhsSource 
VHSDR3 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vhsSource 
VHSv20120926 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vhsSource 
VHSv20130417 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vhsSource 
VHSv20140409 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vhsSource 
VHSv20150108 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
videoSource 
VIDEODR2 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
videoSource 
VIDEODR3 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
videoSource 
VIDEODR4 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
videoSource 
VIDEOv20111208 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vikingSource 
VIKINGDR2 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vikingSource 
VIKINGDR3 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vikingSource 
VIKINGDR4 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vikingSource 
VIKINGv20110714 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vikingSource 
VIKINGv20111019 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vikingSource 
VIKINGv20130417 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vikingSource 
VIKINGv20140402 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vikingSource 
VIKINGv20150421 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vmcSource 
VMCDR1 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vmcSource 
VMCDR2 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vmcSource 
VMCDR3 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vmcSource 
VMCv20110816 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vmcSource 
VMCv20110909 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vmcSource 
VMCv20120126 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vmcSource 
VMCv20121128 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vmcSource 
VMCv20130304 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jAperMagNoAperCorr6 
vmcSource 
VMCv20130805 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vmcSource 
VMCv20140428 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vmcSource 
VMCv20140903 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jAperMagNoAperCorr6 
vmcSource 
VMCv20150309 
Extended source J aperture mag (5.7 arcsec aperture diameter) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jaStratAst 
videoVarFrameSetInfo 
VIDEODR2 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
videoVarFrameSetInfo 
VIDEODR3 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
videoVarFrameSetInfo 
VIDEODR4 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
videoVarFrameSetInfo 
VIDEOv20100513 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
videoVarFrameSetInfo 
VIDEOv20111208 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGDR2 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGDR3 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGDR4 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGv20110714 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGv20111019 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGv20130417 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGv20140402 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vikingVarFrameSetInfo 
VIKINGv20150421 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCDR1 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCDR2 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCDR3 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20110816 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20110909 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20120126 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20121128 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20130304 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20130805 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20140428 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20140903 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vmcVarFrameSetInfo 
VMCv20150309 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratAst 
vvvVarFrameSetInfo 
VVVv20100531 
Strateva parameter, a, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jaStratPht 
videoVarFrameSetInfo 
VIDEODR2 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
videoVarFrameSetInfo 
VIDEODR3 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
videoVarFrameSetInfo 
VIDEODR4 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
videoVarFrameSetInfo 
VIDEOv20100513 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
videoVarFrameSetInfo 
VIDEOv20111208 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGDR2 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGDR3 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGDR4 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGv20110714 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGv20111019 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGv20130417 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGv20140402 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vikingVarFrameSetInfo 
VIKINGv20150421 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCDR1 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCDR2 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCDR3 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20110816 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20110909 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20120126 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20121128 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20130304 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20130805 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20140428 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20140903 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vmcVarFrameSetInfo 
VMCv20150309 
Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jaStratPht 
vvvVarFrameSetInfo 
VVVv20100531 
Strateva parameter, a, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jAverageConf 
svNgc253Source 
SVNGC253v20100429 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
svOrionSource 
SVORIONv20100429 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vhsSource 
VHSDR1 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vhsSource 
VHSDR2 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vhsSource 
VHSDR3 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vhsSource 
VHSv20120926 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
stat.likelihood;em.IR.NIR 
jAverageConf 
vhsSource 
VHSv20130417 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vhsSource 
VHSv20140409 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vhsSource 
VHSv20150108 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vikingSource 
VIKINGDR2 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vikingSource 
VIKINGDR3 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
stat.likelihood;em.IR.NIR 
jAverageConf 
vikingSource 
VIKINGDR4 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vikingSource 
VIKINGv20110714 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vikingSource 
VIKINGv20111019 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vikingSource 
VIKINGv20130417 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vikingSource 
VIKINGv20140402 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vikingSource 
VIKINGv20150421 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vmcSource 
VMCDR2 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vmcSource 
VMCDR3 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vmcSource 
VMCv20110816 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vmcSource 
VMCv20110909 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vmcSource 
VMCv20120126 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vmcSource 
VMCv20121128 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
stat.likelihood;em.IR.NIR 
jAverageConf 
vmcSource 
VMCv20130304 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vmcSource 
VMCv20130805 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vmcSource 
VMCv20140428 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vmcSource 
VMCv20140903 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vmcSource 
VMCv20150309 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.J 
jAverageConf 
vmcSource, vmcSynopticSource 
VMCDR1 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
meta.code 
jAverageConf 
vvvSource 
VVVDR2 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

0.9999995e9 
stat.likelihood;em.IR.NIR 
jAverageConf 
vvvSource, vvvSynopticSource 
VVVDR1 
average confidence in 2 arcsec diameter default aperture (aper3) J 
real 
4 

99999999 
stat.likelihood;em.IR.NIR 
jbestAper 
videoVariability 
VIDEODR2 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
videoVariability 
VIDEODR3 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
videoVariability 
VIDEODR4 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
videoVariability 
VIDEOv20100513 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
videoVariability 
VIDEOv20111208 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGDR2 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGDR3 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGDR4 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGv20110714 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGv20111019 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGv20130417 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGv20140402 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vikingVariability 
VIKINGv20150421 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCDR1 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCDR2 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCDR3 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20110816 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20110909 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20120126 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20121128 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20130304 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20130805 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.NIR 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20140428 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20140903 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
vmcVariability 
VMCv20150309 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 
meta.code.class;em.IR.J 
Aperture magnitude (16) 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) 
jbestAper 
vvvVariability 
VVVv20100531 
Best aperture (16) for photometric statistics in the J band 
int 
4 

9999 

Aperture magnitude (16) 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) 
jbStratAst 
videoVarFrameSetInfo 
VIDEODR2 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
videoVarFrameSetInfo 
VIDEODR3 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
videoVarFrameSetInfo 
VIDEODR4 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
videoVarFrameSetInfo 
VIDEOv20100513 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
videoVarFrameSetInfo 
VIDEOv20111208 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGDR2 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGDR3 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGDR4 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGv20110714 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGv20111019 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGv20130417 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGv20140402 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vikingVarFrameSetInfo 
VIKINGv20150421 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCDR1 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCDR2 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCDR3 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20110816 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20110909 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20120126 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20121128 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20130304 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20130805 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20140428 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20140903 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vmcVarFrameSetInfo 
VMCv20150309 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratAst 
vvvVarFrameSetInfo 
VVVv20100531 
Strateva parameter, b, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jbStratPht 
videoVarFrameSetInfo 
VIDEODR2 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
videoVarFrameSetInfo 
VIDEODR3 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
videoVarFrameSetInfo 
VIDEODR4 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
videoVarFrameSetInfo 
VIDEOv20100513 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
videoVarFrameSetInfo 
VIDEOv20111208 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGDR2 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGDR3 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGDR4 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGv20110714 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGv20111019 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGv20130417 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGv20140402 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vikingVarFrameSetInfo 
VIKINGv20150421 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCDR1 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCDR2 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCDR3 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20110816 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20110909 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20120126 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20121128 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20130304 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20130805 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20140428 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20140903 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vmcVarFrameSetInfo 
VMCv20150309 
Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jbStratPht 
vvvVarFrameSetInfo 
VVVv20100531 
Strateva parameter, b, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqAst 
videoVarFrameSetInfo 
VIDEODR2 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
videoVarFrameSetInfo 
VIDEODR3 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
videoVarFrameSetInfo 
VIDEODR4 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
videoVarFrameSetInfo 
VIDEOv20100513 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
videoVarFrameSetInfo 
VIDEOv20111208 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGDR2 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGDR3 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGDR4 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGv20110714 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGv20111019 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGv20130417 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGv20140402 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vikingVarFrameSetInfo 
VIKINGv20150421 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCDR1 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCDR2 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCDR3 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20110816 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20110909 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20120126 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20121128 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20130304 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20130805 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20140428 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20140903 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vmcVarFrameSetInfo 
VMCv20150309 
Goodness of fit of Strateva function to astrometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqAst 
vvvVarFrameSetInfo 
VVVv20100531 
Goodness of fit of Strateva function to astrometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
videoVariability 
VIDEODR4 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vikingVariability 
VIKINGDR3 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vikingVariability 
VIKINGDR4 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vikingVariability 
VIKINGv20130417 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vikingVariability 
VIKINGv20140402 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vikingVariability 
VIKINGv20150421 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vmcVariability 
VMCDR3 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vmcVariability 
VMCv20140428 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vmcVariability 
VMCv20140903 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
vmcVariability 
VMCv20150309 
Chi square (per degree of freedom) fit to data (mean and expected rms) 
real 
4 

0.9999995e9 
stat.fit.chi2;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqpd 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jchiSqPht 
videoVarFrameSetInfo 
VIDEODR2 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
videoVarFrameSetInfo 
VIDEODR3 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
videoVarFrameSetInfo 
VIDEODR4 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
videoVarFrameSetInfo 
VIDEOv20100513 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
videoVarFrameSetInfo 
VIDEOv20111208 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGDR2 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGDR3 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGDR4 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGv20110714 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGv20111019 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGv20130417 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGv20140402 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vikingVarFrameSetInfo 
VIKINGv20150421 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCDR1 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCDR2 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCDR3 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20110816 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20110909 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20120126 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20121128 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20130304 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20130805 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20140428 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20140903 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vmcVarFrameSetInfo 
VMCv20150309 
Goodness of fit of Strateva function to photometric data in J band 
real 
4 

0.9999995e9 
stat.fit.goodness;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jchiSqPht 
vvvVarFrameSetInfo 
VVVv20100531 
Goodness of fit of Strateva function to photometric data in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jClass 
svNgc253Source 
SVNGC253v20100429 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
svOrionSource 
SVORIONv20100429 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
ultravistaSource, ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vhsSource 
VHSDR2 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vhsSource 
VHSDR3 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vhsSource 
VHSv20120926 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vhsSource 
VHSv20130417 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vhsSource 
VHSv20140409 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vhsSource 
VHSv20150108 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vhsSource, vhsSourceRemeasurement 
VHSDR1 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
videoSource 
VIDEODR2 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
videoSource 
VIDEODR3 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
videoSource 
VIDEODR4 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
videoSource 
VIDEOv20111208 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
videoSource, videoSourceRemeasurement 
VIDEOv20100513 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vikingSource 
VIKINGDR2 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vikingSource 
VIKINGDR3 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vikingSource 
VIKINGDR4 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vikingSource 
VIKINGv20111019 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vikingSource 
VIKINGv20130417 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vikingSource 
VIKINGv20140402 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vikingSource 
VIKINGv20150421 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vikingSource, vikingSourceRemeasurement 
VIKINGv20110714 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCDR2 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCDR3 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vmcSource 
VMCv20110909 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCv20120126 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCv20121128 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCv20130304 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCv20130805 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource 
VMCv20140428 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vmcSource 
VMCv20140903 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vmcSource 
VMCv20150309 
discrete image classification flag in J 
smallint 
2 

9999 
src.class;em.IR.J 
jClass 
vmcSource, vmcSourceRemeasurement 
VMCv20110816 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vmcSource, vmcSynopticSource 
VMCDR1 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vvvSource 
VVVDR2 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vvvSource 
VVVv20110718 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vvvSource, vvvSourceRemeasurement 
VVVv20100531 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClass 
vvvSource, vvvSynopticSource 
VVVDR1 
discrete image classification flag in J 
smallint 
2 

9999 
src.class 
jClassStat 
svNgc253Source 
SVNGC253v20100429 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
svOrionSource 
SVORIONv20100429 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
ultravistaSource 
ULTRAVISTAv20100429 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vhsSource 
VHSDR2 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vhsSource 
VHSDR3 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vhsSource 
VHSv20120926 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vhsSource 
VHSv20130417 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vhsSource 
VHSv20140409 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vhsSource 
VHSv20150108 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vhsSource, vhsSourceRemeasurement 
VHSDR1 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
videoSource 
VIDEODR2 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
videoSource 
VIDEODR3 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
videoSource 
VIDEODR4 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
videoSource 
VIDEOv20100513 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
videoSource 
VIDEOv20111208 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
videoSourceRemeasurement 
VIDEOv20100513 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vikingSource 
VIKINGDR2 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vikingSource 
VIKINGDR3 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vikingSource 
VIKINGDR4 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vikingSource 
VIKINGv20111019 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vikingSource 
VIKINGv20130417 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vikingSource 
VIKINGv20140402 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vikingSource 
VIKINGv20150421 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vikingSource, vikingSourceRemeasurement 
VIKINGv20110714 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCDR2 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCDR3 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vmcSource 
VMCv20110909 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCv20120126 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCv20121128 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCv20130304 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCv20130805 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource 
VMCv20140428 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vmcSource 
VMCv20140903 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vmcSource 
VMCv20150309 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat;em.IR.J 
jClassStat 
vmcSource, vmcSourceRemeasurement 
VMCv20110816 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vmcSource, vmcSynopticSource 
VMCDR1 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSource 
VVVDR1 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSource 
VVVDR2 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSource 
VVVv20100531 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSource 
VVVv20110718 
SExtractor classification statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSourceRemeasurement 
VVVv20100531 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSourceRemeasurement 
VVVv20110718 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSynopticSource 
VVVDR1 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jClassStat 
vvvSynopticSource 
VVVDR2 
N(0,1) stellarnessofprofile statistic in J 
real 
4 

0.9999995e9 
stat 
jcStratAst 
videoVarFrameSetInfo 
VIDEODR2 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
videoVarFrameSetInfo 
VIDEODR3 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
videoVarFrameSetInfo 
VIDEODR4 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
videoVarFrameSetInfo 
VIDEOv20100513 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
videoVarFrameSetInfo 
VIDEOv20111208 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGDR2 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGDR3 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGDR4 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGv20110714 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGv20111019 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGv20130417 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGv20140402 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vikingVarFrameSetInfo 
VIKINGv20150421 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCDR1 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCDR2 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCDR3 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20110816 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20110909 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20120126 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20121128 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20130304 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20130805 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20140428 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20140903 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vmcVarFrameSetInfo 
VMCv20150309 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratAst 
vvvVarFrameSetInfo 
VVVv20100531 
Strateva parameter, c, in fit to astrometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. 
jcStratPht 
videoVarFrameSetInfo 
VIDEODR2 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
videoVarFrameSetInfo 
VIDEODR3 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
videoVarFrameSetInfo 
VIDEODR4 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
videoVarFrameSetInfo 
VIDEOv20100513 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
videoVarFrameSetInfo 
VIDEOv20111208 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGDR2 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGDR3 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGDR4 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGv20110714 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGv20111019 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGv20130417 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGv20140402 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vikingVarFrameSetInfo 
VIKINGv20150421 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCDR1 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCDR2 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCDR3 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20110816 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20110909 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20120126 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20121128 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20130304 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20130805 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20140428 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20140903 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vmcVarFrameSetInfo 
VMCv20150309 
Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. 
real 
4 

0.9999995e9 
stat.fit.param;em.IR.J 
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jcStratPht 
vvvVarFrameSetInfo 
VVVv20100531 
Strateva parameter, c, in fit to photometric rms vs magnitude in J 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 medianabsolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chisquared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236. 
jdate 
twomass_psc 
2MASS 
The Julian Date of the source measurement accurate to +30 seconds. 
float 
8 
Julian days 

time.epoch 
jdate 
twomass_scn 
2MASS 
Julian Date at beginning of scan. 
float 
8 
Julian days 

time.epoch 
jdate 
twomass_sixx2_psc 
2MASS 
julian date of source measurement to +/ 30 sec 
float 
8 
jdate 


jdate 
twomass_sixx2_scn 
2MASS 
Julian date beginning UT of scan data 
float 
8 
jdate 


jdate 
twomass_xsc 
2MASS 
Julian date of the source measurement accurate to +3 minutes. 
float 
8 
Julian days 

time.epoch 
jDeblend 
ultravistaSource, ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vhsSourceRemeasurement 
VHSDR1 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
videoSource, videoSourceRemeasurement 
VIDEOv20100513 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vikingSourceRemeasurement 
VIKINGv20110714 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vikingSourceRemeasurement 
VIKINGv20111019 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vmcSourceRemeasurement 
VMCv20110816 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vmcSourceRemeasurement 
VMCv20110909 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vvvSource 
VVVv20110718 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jDeblend 
vvvSource, vvvSourceRemeasurement 
VVVv20100531 
placeholder flag indicating parent/child relation in J 
int 
4 

99999999 
meta.code 
jEll 
svNgc253Source 
SVNGC253v20100429 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
svOrionSource 
SVORIONv20100429 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
ultravistaSource, ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vhsSource 
VHSDR2 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vhsSource 
VHSDR3 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vhsSource 
VHSv20120926 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vhsSource 
VHSv20130417 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vhsSource 
VHSv20140409 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vhsSource 
VHSv20150108 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vhsSource, vhsSourceRemeasurement 
VHSDR1 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
videoSource 
VIDEODR2 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
videoSource 
VIDEODR3 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
videoSource 
VIDEODR4 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
videoSource 
VIDEOv20111208 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
videoSource, videoSourceRemeasurement 
VIDEOv20100513 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vikingSource 
VIKINGDR2 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vikingSource 
VIKINGDR3 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vikingSource 
VIKINGDR4 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vikingSource 
VIKINGv20111019 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vikingSource 
VIKINGv20130417 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vikingSource 
VIKINGv20140402 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vikingSource 
VIKINGv20150421 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vikingSource, vikingSourceRemeasurement 
VIKINGv20110714 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCDR2 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCDR3 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vmcSource 
VMCv20110909 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCv20120126 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCv20121128 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCv20130304 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCv20130805 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource 
VMCv20140428 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vmcSource 
VMCv20140903 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vmcSource 
VMCv20150309 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity;em.IR.J 
jEll 
vmcSource, vmcSourceRemeasurement 
VMCv20110816 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vmcSource, vmcSynopticSource 
VMCDR1 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vvvSource 
VVVDR2 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vvvSource 
VVVv20110718 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vvvSource, vvvSourceRemeasurement 
VVVv20100531 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jEll 
vvvSource, vvvSynopticSource 
VVVDR1 
1b/a, where a/b=semimajor/minor axes in J 
real 
4 

0.9999995e9 
src.ellipticity 
jeNum 
svNgc253MergeLog 
SVNGC253v20100429 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
svOrionMergeLog 
SVORIONv20100429 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
ultravistaMergeLog 
ULTRAVISTAv20100429 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vhsMergeLog 
VHSDR1 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vhsMergeLog 
VHSDR2 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vhsMergeLog 
VHSDR3 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vhsMergeLog 
VHSv20120926 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vhsMergeLog 
VHSv20130417 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vhsMergeLog 
VHSv20140409 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vhsMergeLog 
VHSv20150108 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
videoMergeLog 
VIDEODR2 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
videoMergeLog 
VIDEODR3 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
videoMergeLog 
VIDEODR4 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
videoMergeLog 
VIDEOv20100513 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
videoMergeLog 
VIDEOv20111208 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGDR2 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGDR3 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGDR4 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vikingMergeLog 
VIKINGv20110714 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGv20111019 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGv20130417 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGv20140402 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vikingMergeLog 
VIKINGv20150421 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vmcMergeLog 
VMCDR2 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCDR3 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vmcMergeLog 
VMCv20110816 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCv20110909 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCv20120126 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCv20121128 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCv20130304 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCv20130805 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vmcMergeLog 
VMCv20140428 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vmcMergeLog 
VMCv20140903 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vmcMergeLog 
VMCv20150309 
the extension number of this J frame 
tinyint 
1 


meta.number;em.IR.J 
jeNum 
vmcMergeLog, vmcSynopticMergeLog 
VMCDR1 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vvvMergeLog 
VVVDR2 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vvvMergeLog 
VVVv20100531 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vvvMergeLog 
VVVv20110718 
the extension number of this J frame 
tinyint 
1 


meta.number 
jeNum 
vvvMergeLog, vvvSynopticMergeLog 
VVVDR1 
the extension number of this J frame 
tinyint 
1 


meta.number 
jErrBits 
svNgc253Source 
SVNGC253v20100429 
processing warning/error bitwise flags in J 
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. 
jErrBits 
svOrionSource 
SVORIONv20100429 
processing warning/error bitwise flags in J 
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. 
jErrBits 
ultravistaSource 
ULTRAVISTAv20100429 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag  Meaning   1  The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).   2  The object was originally blended with another   4  At least one pixel is saturated (or very close to)   8  The object is truncated (too close to an image boundary)   16  Object's aperture data are incomplete or corrupted   32  Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.   64  Memory overflow occurred during deblending   128  Memory overflow occurred during extraction  

jErrBits 
ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vhsSource 
VHSDR1 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vhsSource 
VHSDR2 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vhsSource 
VHSDR3 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vhsSource 
VHSv20120926 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vhsSource 
VHSv20130417 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vhsSource 
VHSv20140409 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vhsSource 
VHSv20150108 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vhsSourceRemeasurement 
VHSDR1 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
videoSource 
VIDEODR2 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag  Meaning   1  The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).   2  The object was originally blended with another   4  At least one pixel is saturated (or very close to)   8  The object is truncated (too close to an image boundary)   16  Object's aperture data are incomplete or corrupted   32  Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.   64  Memory overflow occurred during deblending   128  Memory overflow occurred during extraction  

jErrBits 
videoSource 
VIDEODR3 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag  Meaning   1  The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).   2  The object was originally blended with another   4  At least one pixel is saturated (or very close to)   8  The object is truncated (too close to an image boundary)   16  Object's aperture data are incomplete or corrupted   32  Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.   64  Memory overflow occurred during deblending   128  Memory overflow occurred during extraction  

jErrBits 
videoSource 
VIDEODR4 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag  Meaning   1  The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).   2  The object was originally blended with another   4  At least one pixel is saturated (or very close to)   8  The object is truncated (too close to an image boundary)   16  Object's aperture data are incomplete or corrupted   32  Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.   64  Memory overflow occurred during deblending   128  Memory overflow occurred during extraction  

jErrBits 
videoSource 
VIDEOv20100513 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag  Meaning   1  The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).   2  The object was originally blended with another   4  At least one pixel is saturated (or very close to)   8  The object is truncated (too close to an image boundary)   16  Object's aperture data are incomplete or corrupted   32  Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.   64  Memory overflow occurred during deblending   128  Memory overflow occurred during extraction  

jErrBits 
videoSource 
VIDEOv20111208 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows: Bit Flag  Meaning   1  The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).   2  The object was originally blended with another   4  At least one pixel is saturated (or very close to)   8  The object is truncated (too close to an image boundary)   16  Object's aperture data are incomplete or corrupted   32  Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.   64  Memory overflow occurred during deblending   128  Memory overflow occurred during extraction  

jErrBits 
videoSourceRemeasurement 
VIDEOv20100513 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vikingSource 
VIKINGDR2 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vikingSource 
VIKINGDR3 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vikingSource 
VIKINGDR4 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vikingSource 
VIKINGv20110714 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vikingSource 
VIKINGv20111019 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vikingSource 
VIKINGv20130417 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vikingSource 
VIKINGv20140402 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vikingSource 
VIKINGv20150421 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vikingSourceRemeasurement 
VIKINGv20110714 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vikingSourceRemeasurement 
VIKINGv20111019 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vmcSource 
VMCDR2 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCDR3 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vmcSource 
VMCv20110816 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCv20110909 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCv20120126 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCv20121128 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCv20130304 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCv20130805 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSource 
VMCv20140428 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vmcSource 
VMCv20140903 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vmcSource 
VMCv20150309 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code;em.IR.J 
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture. 
jErrBits 
vmcSource, vmcSynopticSource 
VMCDR1 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vmcSourceRemeasurement 
VMCv20110816 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vmcSourceRemeasurement 
VMCv20110909 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vvvSource 
VVVDR2 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vvvSource 
VVVv20100531 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vvvSource 
VVVv20110718 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vvvSource, vvvSynopticSource 
VVVDR1 
processing warning/error bitwise flags in J 
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. 
jErrBits 
vvvSourceRemeasurement 
VVVv20100531 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jErrBits 
vvvSourceRemeasurement 
VVVv20110718 
processing warning/error bitwise flags in J 
int 
4 

99999999 
meta.code 
jEta 
svNgc253Source 
SVNGC253v20100429 
Offset of J 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. 
jEta 
svOrionSource 
SVORIONv20100429 
Offset of J 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. 
jEta 
ultravistaSource 
ULTRAVISTAv20100429 
Offset of J 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. 
jEta 
vhsSource 
VHSDR1 
Offset of J 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. 
jEta 
vhsSource 
VHSDR2 
Offset of J 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. 
jEta 
vhsSource 
VHSDR3 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vhsSource 
VHSv20120926 
Offset of J 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. 
jEta 
vhsSource 
VHSv20130417 
Offset of J 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. 
jEta 
vhsSource 
VHSv20140409 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vhsSource 
VHSv20150108 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
videoSource 
VIDEODR2 
Offset of J 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. 
jEta 
videoSource 
VIDEODR3 
Offset of J 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. 
jEta 
videoSource 
VIDEODR4 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
videoSource 
VIDEOv20100513 
Offset of J 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. 
jEta 
videoSource 
VIDEOv20111208 
Offset of J 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. 
jEta 
vikingSource 
VIKINGDR2 
Offset of J 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. 
jEta 
vikingSource 
VIKINGDR3 
Offset of J 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. 
jEta 
vikingSource 
VIKINGDR4 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vikingSource 
VIKINGv20110714 
Offset of J 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. 
jEta 
vikingSource 
VIKINGv20111019 
Offset of J 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. 
jEta 
vikingSource 
VIKINGv20130417 
Offset of J 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. 
jEta 
vikingSource 
VIKINGv20140402 
Offset of J 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. 
jEta 
vikingSource 
VIKINGv20150421 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vmcSource 
VMCDR2 
Offset of J 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. 
jEta 
vmcSource 
VMCDR3 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vmcSource 
VMCv20110816 
Offset of J 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. 
jEta 
vmcSource 
VMCv20110909 
Offset of J 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. 
jEta 
vmcSource 
VMCv20120126 
Offset of J 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. 
jEta 
vmcSource 
VMCv20121128 
Offset of J 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. 
jEta 
vmcSource 
VMCv20130304 
Offset of J 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. 
jEta 
vmcSource 
VMCv20130805 
Offset of J 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. 
jEta 
vmcSource 
VMCv20140428 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vmcSource 
VMCv20140903 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vmcSource 
VMCv20150309 
Offset of J detection from master position (+north/south) 
real 
4 
arcsec 
0.9999995e9 
pos.eq.dec;arith.diff;em.IR.J 
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands. 
jEta 
vmcSource, vmcSynopticSource 
VMCDR1 
Offset of J 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. 
jEta 
vvvSource 
VVVDR2 
Offset of J 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. 
jEta 
vvvSource 
VVVv20100531 
Offset of J 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. 
jEta 
vvvSource 
VVVv20110718 
Offset of J 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. 
jEta 
vvvSource, vvvSynopticSource 
VVVDR1 
Offset of J 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. 
jexpML 
videoVarFrameSetInfo 
VIDEODR2 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
videoVarFrameSetInfo 
VIDEODR3 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 
phot.mag;stat.max;em.IR.NIR 
jexpML 
videoVarFrameSetInfo 
VIDEODR4 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
videoVarFrameSetInfo 
VIDEOv20100513 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
videoVarFrameSetInfo 
VIDEOv20111208 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vikingVarFrameSetInfo 
VIKINGDR2 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vikingVarFrameSetInfo 
VIKINGDR3 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;stat.max;em.IR.NIR 
jexpML 
vikingVarFrameSetInfo 
VIKINGDR4 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
vikingVarFrameSetInfo 
VIKINGv20110714 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vikingVarFrameSetInfo 
VIKINGv20111019 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vikingVarFrameSetInfo 
VIKINGv20130417 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;stat.max;em.IR.NIR 
jexpML 
vikingVarFrameSetInfo 
VIKINGv20140402 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max;em.IR.NIR 
jexpML 
vikingVarFrameSetInfo 
VIKINGv20150421 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
vmcVarFrameSetInfo 
VMCDR1 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vmcVarFrameSetInfo 
VMCDR2 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max;em.IR.NIR 
jexpML 
vmcVarFrameSetInfo 
VMCDR3 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
vmcVarFrameSetInfo 
VMCv20110816 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vmcVarFrameSetInfo 
VMCv20110909 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vmcVarFrameSetInfo 
VMCv20120126 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jexpML 
vmcVarFrameSetInfo 
VMCv20121128 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;stat.max;em.IR.NIR 
jexpML 
vmcVarFrameSetInfo 
VMCv20130304 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;stat.max;em.IR.NIR 
jexpML 
vmcVarFrameSetInfo 
VMCv20130805 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max;em.IR.NIR 
jexpML 
vmcVarFrameSetInfo 
VMCv20140428 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
vmcVarFrameSetInfo 
VMCv20140903 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
vmcVarFrameSetInfo 
VMCv20150309 
Expected magnitude limit of frameSet in this in J band. 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.max 
jexpML 
vvvVarFrameSetInfo 
VVVv20100531 
Expected magnitude limit of frameSet in this in J band. 
real 
4 

0.9999995e9 

jExpRms 
videoVariability 
VIDEODR2 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
videoVariability 
VIDEODR3 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
videoVariability 
VIDEODR4 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
videoVariability 
VIDEOv20100513 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
videoVariability 
VIDEOv20111208 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGDR2 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGDR3 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGDR4 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGv20110714 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGv20111019 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGv20130417 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGv20140402 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vikingVariability 
VIKINGv20150421 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCDR1 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCDR2 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCDR3 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20110816 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20110909 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20120126 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20121128 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20130304 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20130805 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20140428 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20140903 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vmcVariability 
VMCv20150309 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jExpRms 
vvvVariability 
VVVv20100531 
Rms calculated from polynomial fit to modal RMS as a function of magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jGausig 
svNgc253Source 
SVNGC253v20100429 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
svOrionSource 
SVORIONv20100429 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
ultravistaSource, ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vhsSource 
VHSDR2 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vhsSource 
VHSDR3 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vhsSource 
VHSv20120926 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vhsSource 
VHSv20130417 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vhsSource 
VHSv20140409 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vhsSource 
VHSv20150108 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vhsSource, vhsSourceRemeasurement 
VHSDR1 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
videoSource 
VIDEODR2 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
videoSource 
VIDEODR3 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
videoSource 
VIDEODR4 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
videoSource 
VIDEOv20111208 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
videoSource, videoSourceRemeasurement 
VIDEOv20100513 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vikingSource 
VIKINGDR2 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vikingSource 
VIKINGDR3 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vikingSource 
VIKINGDR4 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vikingSource 
VIKINGv20111019 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vikingSource 
VIKINGv20130417 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vikingSource 
VIKINGv20140402 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vikingSource 
VIKINGv20150421 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vikingSource, vikingSourceRemeasurement 
VIKINGv20110714 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCDR2 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCDR3 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vmcSource 
VMCv20110909 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCv20120126 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCv20121128 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCv20130304 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCv20130805 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource 
VMCv20140428 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vmcSource 
VMCv20140903 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vmcSource 
VMCv20150309 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param;em.IR.J 
jGausig 
vmcSource, vmcSourceRemeasurement 
VMCv20110816 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vmcSource, vmcSynopticSource 
VMCDR1 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vvvSource 
VVVDR2 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vvvSource 
VVVv20110718 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vvvSource, vvvSourceRemeasurement 
VVVv20100531 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jGausig 
vvvSource, vvvSynopticSource 
VVVDR1 
RMS of axes of ellipse fit in J 
real 
4 
pixels 
0.9999995e9 
src.morph.param 
jHalfRad 
videoSource 
VIDEODR4 
SExtractor halflight radius in J band 
real 
4 
pixels 
0.9999995e9 
phys.angSize;em.IR.J 
jHlCorSMjRadAs 
svNgc253Source 
SVNGC253v20100429 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
ultravistaSource 
ULTRAVISTAv20100429 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vhsSource 
VHSDR1 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vhsSource 
VHSDR2 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vhsSource 
VHSDR3 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;em.IR.J 
jHlCorSMjRadAs 
vhsSource 
VHSv20120926 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize 
jHlCorSMjRadAs 
vhsSource 
VHSv20130417 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize 
jHlCorSMjRadAs 
vhsSource 
VHSv20140409 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;em.IR.J 
jHlCorSMjRadAs 
vhsSource 
VHSv20150108 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;em.IR.J 
jHlCorSMjRadAs 
videoSource 
VIDEODR2 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
videoSource 
VIDEODR3 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize 
jHlCorSMjRadAs 
videoSource 
VIDEODR4 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;em.IR.J 
jHlCorSMjRadAs 
videoSource 
VIDEOv20100513 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
videoSource 
VIDEOv20111208 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vikingSource 
VIKINGDR2 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vikingSource 
VIKINGDR3 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize 
jHlCorSMjRadAs 
vikingSource 
VIKINGDR4 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;em.IR.J 
jHlCorSMjRadAs 
vikingSource 
VIKINGv20110714 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vikingSource 
VIKINGv20111019 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;src 
jHlCorSMjRadAs 
vikingSource 
VIKINGv20130417 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize 
jHlCorSMjRadAs 
vikingSource 
VIKINGv20140402 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize 
jHlCorSMjRadAs 
vikingSource 
VIKINGv20150421 
Seeing corrected halflight, semimajor axis in J band 
real 
4 
arcsec 
0.9999995e9 
phys.angSize;em.IR.J 
jIntRms 
videoVariability 
VIDEODR2 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
videoVariability 
VIDEODR3 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
videoVariability 
VIDEODR4 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
videoVariability 
VIDEOv20100513 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
videoVariability 
VIDEOv20111208 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGDR2 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGDR3 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGDR4 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGv20110714 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGv20111019 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGv20130417 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGv20140402 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vikingVariability 
VIKINGv20150421 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCDR1 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCDR2 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCDR3 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20110816 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20110909 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20120126 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20121128 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20130304 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20130805 
Intrinsic rms in Jband 
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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20140428 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20140903 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vmcVariability 
VMCv20150309 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jIntRms 
vvvVariability 
VVVv20100531 
Intrinsic rms in Jband 
real 
4 
mag 
0.9999995e9 

The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jisDefAst 
videoVarFrameSetInfo 
VIDEODR2 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
videoVarFrameSetInfo 
VIDEODR3 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
videoVarFrameSetInfo 
VIDEODR4 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefAst 
videoVarFrameSetInfo 
VIDEOv20111208 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vikingVarFrameSetInfo 
VIKINGDR2 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vikingVarFrameSetInfo 
VIKINGDR3 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vikingVarFrameSetInfo 
VIKINGDR4 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefAst 
vikingVarFrameSetInfo 
VIKINGv20111019 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vikingVarFrameSetInfo 
VIKINGv20130417 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vikingVarFrameSetInfo 
VIKINGv20140402 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vikingVarFrameSetInfo 
VIKINGv20150421 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefAst 
vmcVarFrameSetInfo 
VMCDR1 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vmcVarFrameSetInfo 
VMCDR2 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vmcVarFrameSetInfo 
VMCDR3 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefAst 
vmcVarFrameSetInfo 
VMCv20110816 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vmcVarFrameSetInfo 
VMCv20110909 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vmcVarFrameSetInfo 
VMCv20120126 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 

jisDefAst 
vmcVarFrameSetInfo 
VMCv20121128 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vmcVarFrameSetInfo 
VMCv20130304 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vmcVarFrameSetInfo 
VMCv20130805 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefAst 
vmcVarFrameSetInfo 
VMCv20140428 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefAst 
vmcVarFrameSetInfo 
VMCv20140903 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefAst 
vmcVarFrameSetInfo 
VMCv20150309 
Use a default model for the astrometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
videoVarFrameSetInfo 
VIDEODR2 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
videoVarFrameSetInfo 
VIDEODR3 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
videoVarFrameSetInfo 
VIDEODR4 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
videoVarFrameSetInfo 
VIDEOv20111208 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vikingVarFrameSetInfo 
VIKINGDR2 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vikingVarFrameSetInfo 
VIKINGDR3 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vikingVarFrameSetInfo 
VIKINGDR4 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
vikingVarFrameSetInfo 
VIKINGv20111019 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vikingVarFrameSetInfo 
VIKINGv20130417 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vikingVarFrameSetInfo 
VIKINGv20140402 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vikingVarFrameSetInfo 
VIKINGv20150421 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
vmcVarFrameSetInfo 
VMCDR1 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vmcVarFrameSetInfo 
VMCDR2 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vmcVarFrameSetInfo 
VMCDR3 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
vmcVarFrameSetInfo 
VMCv20110816 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vmcVarFrameSetInfo 
VMCv20110909 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vmcVarFrameSetInfo 
VMCv20120126 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 

jisDefPht 
vmcVarFrameSetInfo 
VMCv20121128 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vmcVarFrameSetInfo 
VMCv20130304 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vmcVarFrameSetInfo 
VMCv20130805 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.NIR 
jisDefPht 
vmcVarFrameSetInfo 
VMCv20140428 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
vmcVarFrameSetInfo 
VMCv20140903 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jisDefPht 
vmcVarFrameSetInfo 
VMCv20150309 
Use a default model for the photometric noise in J band. 
tinyint 
1 

0 
meta.code;em.IR.J 
jitterID 
Multiframe 
SVNGC253v20100429 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
SVORIONv20100429 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
ULTRAVISTAv20100429 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSDR1 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSDR2 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSDR3 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSv20120926 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSv20130417 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSv20140409 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VHSv20150108 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIDEODR2 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIDEODR3 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIDEODR4 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIDEOv20100513 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIDEOv20111208 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGDR2 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGDR3 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGDR4 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGv20110714 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGv20111019 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGv20130417 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGv20140402 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VIKINGv20150421 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCDR1 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCDR2 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCDR3 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20110816 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20110909 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20120126 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20121128 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20130304 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20130805 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20140428 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20140903 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VMCv20150309 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VSAQC 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VVVDR1 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VVVDR2 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VVVv20100531 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
Multiframe 
VVVv20110718 
Sequence number of jitter {image primary HDU keyword: JITTER_I} 
smallint 
2 

9999 

jitterID 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Sequence number of jitter 
smallint 
2 

9999 

jitterName 
Multiframe 
SVNGC253v20100429 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
SVORIONv20100429 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
ULTRAVISTAv20100429 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSDR1 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSDR2 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSDR3 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSv20120926 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSv20130417 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSv20140409 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VHSv20150108 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIDEODR2 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIDEODR3 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIDEODR4 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIDEOv20100513 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIDEOv20111208 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGDR2 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGDR3 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGDR4 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGv20110714 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGv20111019 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGv20130417 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGv20140402 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VIKINGv20150421 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCDR1 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCDR2 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCDR3 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20110816 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20110909 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20120126 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20121128 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20130304 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20130805 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20140428 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20140903 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VMCv20150309 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VSAQC 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VVVDR1 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VVVDR2 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VVVv20100531 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
Multiframe 
VVVv20110718 
Name of jitter pattern {image primary HDU keyword: JITTR_ID} 
varchar 
8 

NONE 

jitterName 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Name of jitter pattern 
varchar 
8 

NONE 

jitterX 
Multiframe 
SVNGC253v20100429 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
SVORIONv20100429 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
ULTRAVISTAv20100429 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSDR1 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSDR2 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSDR3 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSv20120926 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSv20130417 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSv20140409 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VHSv20150108 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIDEODR2 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIDEODR3 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIDEODR4 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIDEOv20100513 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIDEOv20111208 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGDR2 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGDR3 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGDR4 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGv20110714 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGv20111019 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGv20130417 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGv20140402 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VIKINGv20150421 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCDR1 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCDR2 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCDR3 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20110816 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20110909 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20120126 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20121128 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20130304 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20130805 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20140428 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20140903 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VMCv20150309 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VSAQC 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VVVDR1 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VVVDR2 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VVVv20100531 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
Multiframe 
VVVv20110718 
X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} 
real 
4 

0.9999995e9 

jitterX 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
X offset in jitter pattern [arcsec] 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
SVNGC253v20100429 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
SVORIONv20100429 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
ULTRAVISTAv20100429 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSDR1 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSDR2 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSDR3 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSv20120926 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSv20130417 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSv20140409 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VHSv20150108 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIDEODR2 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIDEODR3 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIDEODR4 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIDEOv20100513 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIDEOv20111208 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGDR2 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGDR3 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGDR4 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGv20110714 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGv20111019 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGv20130417 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGv20140402 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VIKINGv20150421 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCDR1 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCDR2 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCDR3 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20110816 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20110909 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20120126 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20121128 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20130304 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20130805 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20140428 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20140903 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VMCv20150309 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VSAQC 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VVVDR1 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VVVDR2 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VVVv20100531 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
Multiframe 
VVVv20110718 
Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} 
real 
4 

0.9999995e9 

jitterY 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Y offset in jitter pattern [arcsec] 
real 
4 

0.9999995e9 

jKronMag 
videoSource 
VIDEODR4 
Extended source J mag (Kron  SExtractor MAG_AUTO) 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J 
jKronMagErr 
videoSource 
VIDEODR4 
Extended source J mag error (Kron  SExtractor MAG_AUTO) 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;phot.mag 
jksiWS 
vmcVariability 
VMCDR1 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 

The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCDR2 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCDR3 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param;em.IR.J;em.IR.K 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20110816 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 

The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20110909 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 

The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20120126 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 

The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20121128 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20130304 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20130805 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20140428 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param;em.IR.J;em.IR.K 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20140903 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param;em.IR.J;em.IR.K 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
jksiWS 
vmcVariability 
VMCv20150309 
WelchStetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections 
real 
4 

0.9999995e9 
stat.param;em.IR.J;em.IR.K 
The WelchStetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands. 
Jmag 
mcps_lmcSource, mcps_smcSource 
MCPS 
The J band magnitude (from 2MASS) (0.00 if star not detected.) 
real 
4 
mag 


jMag 
ukirtFSstars 
SVNGC253v20100429 
J band total magnitude on the MKO(UFTI) system 
real 
4 
mag 

phot.mag 
jMag 
ukirtFSstars 
SVORIONv20100429 
J band total magnitude on the MKO(UFTI) system 
real 
4 
mag 

phot.mag 
jMag 
ukirtFSstars 
ULTRAVISTAv20100429 
J band total magnitude on the MKO(UFTI) system 
real 
4 
mag 

phot.mag 
jMag 
ukirtFSstars 
VIDEOv20100513 
J band total magnitude on the MKO(UFTI) system 
real 
4 
mag 

phot.mag 
jMag 
ukirtFSstars 
VIKINGv20110714 
J band total magnitude on the MKO(UFTI) system 
real 
4 
mag 

phot.mag 
jMag 
ukirtFSstars 
VVVv20100531 
J band total magnitude on the MKO(UFTI) system 
real 
4 
mag 

phot.mag 
jMag 
ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vhsSourceRemeasurement 
VHSDR1 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
videoSourceRemeasurement 
VIDEOv20100513 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vikingSourceRemeasurement 
VIKINGv20110714 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vikingSourceRemeasurement 
VIKINGv20111019 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vmcSourceRemeasurement 
VMCv20110816 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vmcSourceRemeasurement 
VMCv20110909 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vvvSourceRemeasurement 
VVVv20100531 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
jMag 
vvvSourceRemeasurement 
VVVv20110718 
J mag (as appropriate for this merged source) 
real 
4 
mag 
0.9999995e9 
phot.mag 
Jmag2MASS 
spitzer_smcSource 
SPITZER 
The 2MASS J band magnitude. 
real 
4 
mag 


jMagErr 
ukirtFSstars 
SVNGC253v20100429 
J band magnitude error 
real 
4 
mag 

stat.error 
jMagErr 
ukirtFSstars 
SVORIONv20100429 
J band magnitude error 
real 
4 
mag 

stat.error 
jMagErr 
ukirtFSstars 
ULTRAVISTAv20100429 
J band magnitude error 
real 
4 
mag 

stat.error 
jMagErr 
ukirtFSstars 
VIDEOv20100513 
J band magnitude error 
real 
4 
mag 

stat.error 
jMagErr 
ukirtFSstars 
VIKINGv20110714 
J band magnitude error 
real 
4 
mag 

stat.error 
jMagErr 
ukirtFSstars 
VVVv20100531 
J band magnitude error 
real 
4 
mag 

stat.error 
jMagErr 
ultravistaSourceRemeasurement 
ULTRAVISTAv20100429 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vhsSourceRemeasurement 
VHSDR1 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
videoSourceRemeasurement 
VIDEOv20100513 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vikingSourceRemeasurement 
VIKINGv20110714 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vikingSourceRemeasurement 
VIKINGv20111019 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vmcSourceRemeasurement 
VMCv20110816 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vmcSourceRemeasurement 
VMCv20110909 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vvvSourceRemeasurement 
VVVv20100531 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagErr 
vvvSourceRemeasurement 
VVVv20110718 
Error in J mag 
real 
4 
mag 
0.9999995e9 
stat.error 
jMagMAD 
videoVariability 
VIDEODR2 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
videoVariability 
VIDEODR3 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
videoVariability 
VIDEODR4 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
videoVariability 
VIDEOv20100513 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
videoVariability 
VIDEOv20111208 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGDR2 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGDR3 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGDR4 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGv20110714 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGv20111019 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGv20130417 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGv20140402 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vikingVariability 
VIKINGv20150421 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCDR1 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCDR2 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCDR3 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20110816 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20110909 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20120126 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20121128 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20130304 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20130805 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20140428 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20140903 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vmcVariability 
VMCv20150309 
Median Absolute Deviation of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.err;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagMAD 
vvvVariability 
VVVv20100531 
Median Absolute Deviation of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
videoVariability 
VIDEODR2 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
videoVariability 
VIDEODR3 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
videoVariability 
VIDEODR4 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
videoVariability 
VIDEOv20100513 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
videoVariability 
VIDEOv20111208 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGDR2 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGDR3 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGDR4 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGv20110714 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGv20111019 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGv20130417 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGv20140402 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vikingVariability 
VIKINGv20150421 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCDR1 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCDR2 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCDR3 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20110816 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20110909 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20120126 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20121128 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20130304 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20130805 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20140428 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20140903 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vmcVariability 
VMCv20150309 
rms of J magnitude 
real 
4 
mag 
0.9999995e9 
stat.error;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMagRms 
vvvVariability 
VVVv20100531 
rms of J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
vikingVariability 
VIKINGDR3 
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. 
jmaxCadence 
vikingVariability 
VIKINGDR4 
maximum gap between observations 
real 
4 
days 
0.9999995e9 
time.interval;obs;stat.max;em.IR.J 
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
vikingVariability 
VIKINGv20130417 
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. 
jmaxCadence 
vikingVariability 
VIKINGv20140402 
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. 
jmaxCadence 
vikingVariability 
VIKINGv20150421 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
vmcVariability 
VMCv20140428 
maximum gap between observations 
real 
4 
days 
0.9999995e9 
time.interval;obs;stat.max;em.IR.J 
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jmaxCadence 
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. 
jMaxMag 
videoVariability 
VIDEODR2 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
videoVariability 
VIDEODR3 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
videoVariability 
VIDEODR4 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
videoVariability 
VIDEOv20100513 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
videoVariability 
VIDEOv20111208 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGDR2 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGDR3 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGDR4 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGv20110714 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGv20111019 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGv20130417 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGv20140402 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vikingVariability 
VIKINGv20150421 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCDR1 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCDR2 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCDR3 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20110816 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20110909 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20120126 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20121128 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20130304 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20130805 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20140428 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20140903 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vmcVariability 
VMCv20150309 
Maximum magnitude in J band, of good detections 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jMaxMag 
vvvVariability 
VVVv20100531 
Maximum magnitude in J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
videoVariability 
VIDEODR2 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
videoVariability 
VIDEODR3 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
videoVariability 
VIDEODR4 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
videoVariability 
VIDEOv20100513 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
videoVariability 
VIDEOv20111208 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGDR2 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGDR3 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGDR4 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGv20110714 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGv20111019 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGv20130417 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGv20140402 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vikingVariability 
VIKINGv20150421 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCDR1 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCDR2 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCDR3 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20110816 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20110909 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20120126 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20121128 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20130304 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20130805 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20140428 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20140903 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vmcVariability 
VMCv20150309 
Mean J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.mean;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmeanMag 
vvvVariability 
VVVv20100531 
Mean J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
vikingVariability 
VIKINGDR3 
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. 
jmedCadence 
vikingVariability 
VIKINGDR4 
median gap between observations 
real 
4 
days 
0.9999995e9 
time.interval;obs;stat.median;em.IR.J 
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
vikingVariability 
VIKINGv20130417 
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. 
jmedCadence 
vikingVariability 
VIKINGv20140402 
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. 
jmedCadence 
vikingVariability 
VIKINGv20150421 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
vmcVariability 
VMCv20140428 
median gap between observations 
real 
4 
days 
0.9999995e9 
time.interval;obs;stat.median;em.IR.J 
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedCadence 
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. 
jmedianMag 
videoVariability 
VIDEODR2 
Median J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmedianMag 
videoVariability 
VIDEODR3 
Median J 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 chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmedianMag 
videoVariability 
VIDEODR4 
Median J magnitude 
real 
4 
mag 
0.9999995e9 
phot.mag;em.IR.J;stat.median;em.IR.J 
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chisquared is calculated, assuming a nonvariable object which has the noise from the expectedrms and mean calculated as above. The probVar statistic assumes a chisquared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3. 
jmedianMag 
videoVariability 
VIDEOv20100513 
Median J magnitude 
real 
4 
mag 