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Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20150413 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20150413 Schema Browser (other Browser versions).
A B C D E F G H I J K L M
N O P Q R S T U V W X Y Z

J

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified Content Descriptor
J twomass SIXDF J magnitude (JEXT) used for J selection real 4 mag    
j_2mrat twomass_scn 2MASS J-band 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 5-sigma isophote. real 4     phys.size.axisRatio
j_5sig_phi twomass_xsc 2MASS J angle to 5-sigma 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 3-sigma 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 bi-symmetric cross-correlation chi. real 4     stat.fit.param
j_bisym_rat twomass_xsc 2MASS J bi-symmetric 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 % chi-fraction for elliptical fit to 3-sig isophote. real 4     stat.fit.param
j_cmsig twomass_psc 2MASS Corrected photometric uncertainty for the default J-band magnitude. real 4 mag J-band 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 5-sigma to 3-sigma 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 J-H color, computed from the J-band and H-band 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 J-Ks color, computed from the J-band and Ks-band 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 J-band 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 J-band 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 J-band magnitude entry is "null". float 8 mag    
j_m_2mass wise_allskysc WISE 2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source.
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is default.
real 4 mag -0.9999995e9  
j_m_2mass wise_prelimsc WISE 2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band 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 J-band "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 half-light 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 1-sigma uncertainty in 10 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_15 twomass_xsc 2MASS J 1-sigma uncertainty in 15 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_20 twomass_xsc 2MASS J 1-sigma uncertainty in 20 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_25 twomass_xsc 2MASS J 1-sigma uncertainty in 25 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_2mass allwise_sc WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is "null". float 8 mag    
j_msig_2mass wise_allskysc WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source.
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is default.
real 4 mag -0.9999995e9  
j_msig_2mass wise_prelimsc WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is default
real 4 mag -0.9999995e9  
j_msig_30 twomass_xsc 2MASS J 1-sigma uncertainty in 30 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_40 twomass_xsc 2MASS J 1-sigma uncertainty in 40 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_5 twomass_xsc 2MASS J 1-sigma uncertainty in 5 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_50 twomass_xsc 2MASS J 1-sigma uncertainty in 50 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_60 twomass_xsc 2MASS J 1-sigma uncertainty in 60 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_7 twomass_sixx2_xsc 2MASS J 1-sigma uncertainty in 7 arcsec circular ap. mag real 4 mag    
j_msig_7 twomass_xsc 2MASS J 1-sigma uncertainty in 7 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_70 twomass_xsc 2MASS J 1-sigma uncertainty in 70 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_c twomass_xsc 2MASS J 1-sigma uncertainty in Kron circular mag. real 4 mag   stat.error
j_msig_e twomass_xsc 2MASS J 1-sigma uncertainty in Kron elliptical mag. real 4 mag   stat.error
j_msig_ext twomass_sixx2_xsc 2MASS J 1-sigma uncertainty in mag from fit extrapolation real 4 mag    
j_msig_ext twomass_xsc 2MASS J 1-sigma uncertainty in mag from fit extrapolation. real 4 mag   stat.error
j_msig_fc twomass_xsc 2MASS J 1-sigma uncertainty in fiducial Kron circ. mag. real 4 mag   stat.error
j_msig_fe twomass_xsc 2MASS J 1-sigma uncertainty in fiducial Kron ell. mag. real 4 mag   stat.error
j_msig_i20c twomass_xsc 2MASS J 1-sigma uncertainty in 20mag/sq." iso. circ. mag. real 4 mag   stat.error
j_msig_i20e twomass_xsc 2MASS J 1-sigma uncertainty in 20mag/sq." iso. ell. mag. real 4 mag   stat.error
j_msig_i21c twomass_xsc 2MASS J 1-sigma uncertainty in 21mag/sq." iso. circ. mag. real 4 mag   stat.error
j_msig_i21e twomass_xsc 2MASS J 1-sigma uncertainty in 21mag/sq." iso. ell. mag. real 4 mag   stat.error
j_msig_j21fc twomass_xsc 2MASS J 1-sigma uncertainty in 21mag/sq." iso.fid.circ.mag. real 4 mag   stat.error
j_msig_j21fe twomass_xsc 2MASS J 1-sigma uncertainty in 21mag/sq." iso.fid.ell.mag. real 4 mag   stat.error
j_msig_k20fc twomass_xsc 2MASS J 1-sigma uncertainty in 20mag/sq." iso.fid.circ. mag. real 4 mag   stat.error
j_msig_k20fe twomass_xsc 2MASS J 1-sigma uncertainty in 20mag/sq." iso.fid.ell.mag. real 4 mag   stat.error
j_msig_stdap twomass_psc 2MASS Uncertainty in the J-band standard aperture magnitude. real 4 mag   phot.flux
j_msig_sys twomass_xsc 2MASS J 1-sigma uncertainty in system photometry mag. real 4 mag   stat.error
j_msigcom twomass_psc 2MASS Combined, or total photometric uncertainty for the default J-band magnitude. real 4 mag J-band phot.flux
j_msigcom twomass_sixx2_psc 2MASS combined (total) J band photometric uncertainty real 4 mag    
j_msnr10 twomass_scn 2MASS The estimated J-band 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 J-band 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 5-sigma to 3-sigma percent area change. smallint 2     FIT_PARAM
j_phi twomass_xsc 2MASS J angle to 3-sigma major axis (E of N). smallint 2 degrees   pos.posAng
j_psfchi twomass_psc 2MASS Reduced chi-squared goodness-of-fit value for the J-band profile-fit photometry made on the 1.3 s "Read_2" exposures. real 4     stat.fit.param
j_psp twomass_scn 2MASS J-band 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 Base-10 logarithm of the mode of the noise distribution for all point source detections in the scan, where the noise is estimated from the measured J-band 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 semi-major axis. real 4 arcsec   phys.angSize;src
j_r_eff twomass_xsc 2MASS J half-light (integrated half-flux 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. semi-major 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. semi-major 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 J-band 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 RMS-error of J-band 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 J-band "scan" signal-to-noise ratio. real 4 mag   instr.det.noise
j_snr twomass_sixx2_psc 2MASS J band "scan" signal-to-noise ratio real 4      
j_subst2 twomass_xsc 2MASS J residual background #2 (score). real 4     meta.code
j_zp_ap twomass_scn 2MASS Photometric zero-point for J-band aperture photometry. real 4 mag   phot.mag;arith.zp
j_zp_ap twomass_sixx2_scn 2MASS J band ap. calibration photometric zero-point 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR3 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR4 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEOv20100513 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEOv20111208 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR2 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR3 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR4 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20110714 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20111019 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20130417 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20140402 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20150421 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR1 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR2 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR3 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20110816 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20110909 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20120126 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20121128 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20130304 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20130805 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20140428 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20140903 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20150309 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vvvVariability VVVv20100531 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat svOrionSource SVORIONv20100429 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat ultravistaSource ULTRAVISTAv20100429 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat ultravistaSourceRemeasurement ULTRAVISTAv20100429 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSDR3 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20120926 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSv20130417 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSv20140409 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20150108 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource, vhsSourceRemeasurement VHSDR1 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEODR2 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEODR3 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEODR4 S-Extractor classification statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat videoSource VIDEOv20100513 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEOv20111208 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSourceRemeasurement VIDEOv20100513 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGDR3 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGDR4 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20111019 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGv20130417 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGv20140402 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGv20150421 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource, vikingSourceRemeasurement VIKINGv20110714 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCDR3 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20110909 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20120126 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20121128 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20130304 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20130805 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20140428 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20140903 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20150309 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource, vmcSourceRemeasurement VMCv20110816 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource, vmcSynopticSource VMCDR1 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVDR1 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVDR2 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVv20100531 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVv20110718 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSourceRemeasurement VVVv20100531 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSourceRemeasurement VVVv20110718 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSynopticSource VVVDR1 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSynopticSource VVVDR2 N(0,1) stellarness-of-profile 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared 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 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll svOrionSource SVORIONv20100429 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTAv20100429 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSDR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20120926 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSv20130417 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSv20140409 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20150108 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource, vhsSourceRemeasurement VHSDR1 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll videoSource VIDEOv20111208 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource, videoSourceRemeasurement VIDEOv20100513 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20111019 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20130417 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20140402 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20150421 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource, vikingSourceRemeasurement VIKINGv20110714 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCDR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20110909 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20120126 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20121128 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20130304 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20130805 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20140428 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20140903 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20150309 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource, vmcSourceRemeasurement VMCv20110816 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource, vmcSynopticSource VMCDR1 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource VVVDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource VVVv20110718 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource, vvvSourceRemeasurement VVVv20100531 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource, vvvSynopticSource VVVDR1 1-b/a, where a/b=semi-major/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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

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 FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 half-light radius in J band real 4 pixels -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs svNgc253Source SVNGC253v20100429 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs ultravistaSource ULTRAVISTAv20100429 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vhsSource VHSDR1 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vhsSource VHSDR2 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vhsSource VHSDR3 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20120926 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vhsSource VHSv20130417 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vhsSource VHSv20140409 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20150108 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs videoSource VIDEODR2 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs videoSource VIDEODR3 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs videoSource VIDEODR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs videoSource VIDEOv20100513 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs videoSource VIDEOv20111208 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGDR2 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGDR3 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vikingSource VIKINGDR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vikingSource VIKINGv20110714 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGv20111019 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGv20130417 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vikingSource VIKINGv20140402 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vikingSource VIKINGv20150421 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jIntRms videoVariability VIDEODR2 Intrinsic rms 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCDR2 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCDR3 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20110816 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20110909 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20120126 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20121128 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20130304 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20130805 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20140428 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20140903 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20150309 Welch-Stetson 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 Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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 chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared 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