gdr2ap.mainivo://org.gavo.dc/gdr2ap/q/mainThe GAVO DC teamFouesneau, M.Andrae, R.Dharmawardena, T.Rybizki, J.Bailer-Jones, C. A. L.Demleitner, M.2022-05-04T09:51:29ZGAVO Data Centre Team

Mönchhofstrasse 12-14, D-69120 Heidelberg

gavo@ari.uni-heidelberg.de+49 6221 54 1837stellar-propertiesmilky-way-galaxyinterstellar-dust We estimated the stellar astrophysical parameters of 120 million stars over the entire sky that have Gaia parallax and photometry from Gaia DR2, 2MASS, and AllWISE. We provide estimates of log age, log mass, log temperature, log luminosity, log surface gravity, distance modulus, dust extinction (A0), and average grain size (R0) along the lines of sight. In contrast with other catalogs, we do not use a Galactic model as prior but weakly informative ones. Our estimate and uncertainties are quantiles, so they are invariant under monotonic transformations (e.g., log, exp). This means that one can use our median estimate to obtain the median distance or temperature, for instance, and likewise for the uncertainties.2022arXiv220103252Fhttps://dc.g-vo.org/tableinfo/gdr2ap.mainCatalogResearchGaia0/0-1157174 571741.986e-19 4.966e-19Opticalgdr2ap We estimated the stellar astrophysical parameters of 120 million stars over the entire sky that have Gaia parallax and photometry from Gaia DR2, 2MASS, and AllWISE. We provide estimates of log age, log mass, log temperature, log luminosity, log surface gravity, distance modulus, dust extinction (A0), and average grain size (R0) along the lines of sight. In contrast with other catalogs, we do not use a Galactic model as prior but weakly informative ones. Our estimate and uncertainties are quantiles, so they are invariant under monotonic transformations (e.g., log, exp). This means that one can use our median estimate to obtain the median distance or temperature, for instance, and likewise for the uncertainties.gdr2ap.main We estimated the stellar astrophysical parameters of 120 million stars over the entire sky that have Gaia parallax and photometry from Gaia DR2, 2MASS, and AllWISE. We provide estimates of log age, log mass, log temperature, log luminosity, log surface gravity, distance modulus, dust extinction (A0), and average grain size (R0) along the lines of sight. In contrast with other catalogs, we do not use a Galactic model as prior but weakly informative ones. Our estimate and uncertainties are quantiles, so they are invariant under monotonic transformations (e.g., log, exp). This means that one can use our median estimate to obtain the median distance or temperature, for instance, and likewise for the uncertainties.130000000source_idUnique source identifier. Note that this *cannot* be matched against the DR1 source_id.meta.id;meta.mainlongindexedprimarya0_bestmultivariate maximum posterior estimate for the dust exctinction A₀ towards this source.magphys.absorptionfloatnullablea0_p50median of the distribution of dust exctinction A₀ towards this source.magphys.absorption;stat.medianfloatindexednullablea0_distDistribution (min, p16, p25, p50, p75, p84, max) of the dust exctinction A₀ towards this source. In ADQL, write a0_dist[1] for the minimum, a0_dist[2] for the 16th percentile, and so on.magstat;phys.absorptionfloatnullabler0_bestmultivariate maximum posterior estimate for the average dust grain size extinction parameter.phys.absorptionfloatnullabler0_p50median of the distribution of average dust grain size extinction parameter.phys.absorption;stat.medianfloatindexednullabler0_distDistribution (min, p16, p25, p50, p75, p84, max) of the average dust grain size extinction parameter. In ADQL, write r0_dist[1] for the minimum, r0_dist[2] for the 16th percentile, and so on.stat;phys.absorptionfloatnullableloga_bestmultivariate maximum posterior estimate for the log10 of the age.log(yr)time.agefloatnullableloga_p50median of the distribution of log10 of the age.log(yr)time.age;stat.medianfloatnullableloga_distDistribution (min, p16, p25, p50, p75, p84, max) of the log10 of the age. In ADQL, write loga_dist[1] for the minimum, loga_dist[2] for the 16th percentile, and so on.log(yr)stat;time.agefloatnullablelogl_bestmultivariate maximum posterior estimate for the log10 of the luminosity.log(solLum)phys.luminosityfloatnullablelogl_p50median of the distribution of log10 of the luminosity.log(solLum)phys.luminosity;stat.medianfloatindexednullablelogl_distDistribution (min, p16, p25, p50, p75, p84, max) of the log10 of the luminosity. In ADQL, write logl_dist[1] for the minimum, logl_dist[2] for the 16th percentile, and so on.log(solLum)stat;phys.luminosityfloatnullablelogm_bestmultivariate maximum posterior estimate for the log10 of the mass.log(solMass)phys.massfloatnullablelogm_p50median of the distribution of log10 of the mass.log(solMass)phys.mass;stat.medianfloatnullablelogm_distDistribution (min, p16, p25, p50, p75, p84, max) of the log10 of the mass. In ADQL, write logm_dist[1] for the minimum, logm_dist[2] for the 16th percentile, and so on.log(solMass)stat;phys.massfloatnullablelogt_bestmultivariate maximum posterior estimate for the log10 of the effective temperature.log(K)phys.temperaturefloatnullablelogt_p50median of the distribution of log10 of the effective temperature.log(K)phys.temperature;stat.medianfloatindexednullablelogt_distDistribution (min, p16, p25, p50, p75, p84, max) of the log10 of the effective temperature. In ADQL, write logt_dist[1] for the minimum, logt_dist[2] for the 16th percentile, and so on.log(K)stat;phys.temperaturefloatnullablelogg_bestmultivariate maximum posterior estimate for the log10 of the surface gravity.log(cm/s**2)phys.gravityfloatnullablelogg_p50median of the distribution of log10 of the surface gravity.log(cm/s**2)phys.gravity;stat.medianfloatnullablelogg_distDistribution (min, p16, p25, p50, p75, p84, max) of the log10 of the surface gravity. In ADQL, write logg_dist[1] for the minimum, logg_dist[2] for the 16th percentile, and so on.log(cm/s**2)stat;phys.gravityfloatnullablea_bp_bestmultivariate maximum posterior estimate for the attenuation in the Gaia BP band towards this source..magphys.absorption;em.opt.Bfloatnullablea_bp_p50median of the distribution of attenuation in the Gaia BP band towards this source..magphys.absorption;em.opt.B;stat.medianfloatnullablea_bp_distDistribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia BP band towards this source.. In ADQL, write a_bp_dist[1] for the minimum, a_bp_dist[2] for the 16th percentile, and so on.magstat;phys.absorption;em.opt.Bfloatnullablea_g_bestmultivariate maximum posterior estimate for the attenuation in the Gaia G band towards this source..magphys.absorption;em.optfloatnullablea_g_p50median of the distribution of attenuation in the Gaia G band towards this source..magphys.absorption;em.opt;stat.medianfloatnullablea_g_distDistribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia G band towards this source.. In ADQL, write a_g_dist[1] for the minimum, a_g_dist[2] for the 16th percentile, and so on.magstat;phys.absorption;em.optfloatnullablea_rp_bestmultivariate maximum posterior estimate for the attenuation in the Gaia RP band towards this source..magphys.absorptionfloatnullablea_rp_p50median of the distribution of attenuation in the Gaia RP band towards this source..magphys.absorption;stat.medianfloatnullablea_rp_distDistribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia RP band towards this source.. In ADQL, write a_rp_dist[1] for the minimum, a_rp_dist[2] for the 16th percentile, and so on.magstat;phys.absorptionfloatnullablemag_bpRecalibrated Gaia BP magnitude.magphot.mag;em.opt.Bfloatnullableerr_bpRecalibrated error from original Gaia BP magnitude.magstat.error;phot.mag;em.opt.Bfloatnullablebp_bestmultivariate maximum posterior estimate for the Gaia BP magnitude.magphot.mag;em.opt.Bfloatnullablebp_p50median of the distribution of Gaia BP magnitude.magphot.mag;em.opt.B;stat.medianfloatnullablebp_distDistribution (min, p16, p25, p50, p75, p84, max) of the Gaia BP magnitude. In ADQL, write bp_dist[1] for the minimum, bp_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.opt.Bfloatnullablemag_gRecalibrated Gaia G magnitude.magphot.mag;em.optfloatnullableerr_gRecalibrated error from original Gaia G magnitude.magstat.error;phot.mag;em.optfloatnullableg_bestmultivariate maximum posterior estimate for the Gaia G magnitude.magphot.mag;em.optfloatnullableg_p50median of the distribution of Gaia G magnitude.magphot.mag;em.opt;stat.medianfloatnullableg_distDistribution (min, p16, p25, p50, p75, p84, max) of the Gaia G magnitude. In ADQL, write g_dist[1] for the minimum, g_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.optfloatnullablemag_rpRecalibrated Gaia RP magnitude.magphot.mag;em.opt.Rfloatnullableerr_rpRecalibrated error from original Gaia RP magnitude.magstat.error;phot.mag;em.opt.Rfloatnullablerp_bestmultivariate maximum posterior estimate for the Gaia RP magnitude.magphot.mag;em.opt.Rfloatnullablerp_p50median of the distribution of Gaia RP magnitude.magphot.mag;em.opt.R;stat.medianfloatnullablerp_distDistribution (min, p16, p25, p50, p75, p84, max) of the Gaia RP magnitude. In ADQL, write rp_dist[1] for the minimum, rp_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.opt.Rfloatnullablemag_jRecalibrated 2MASS J magnitude.magphot.mag;em.IR.Jfloatnullableerr_jRecalibrated error from original 2MASS J magnitude.magstat.error;phot.mag;em.IR.Jfloatnullablej_bestmultivariate maximum posterior estimate for the 2MASS J magnitude.magphot.mag;em.IR.Jfloatnullablej_p50median of the distribution of 2MASS J magnitude.magphot.mag;em.IR.J;stat.medianfloatnullablej_distDistribution (min, p16, p25, p50, p75, p84, max) of the 2MASS J magnitude. In ADQL, write j_dist[1] for the minimum, j_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.IR.Jfloatnullablemag_hRecalibrated 2MASS H magnitude.magphot.mag;em.IR.Hfloatnullableerr_hRecalibrated error from original 2MASS H magnitude.magstat.error;phot.mag;em.IR.Hfloatnullableh_bestmultivariate maximum posterior estimate for the 2MASS H magnitude.magphot.mag;em.IR.Hfloatnullableh_p50median of the distribution of 2MASS H magnitude.magphot.mag;em.IR.H;stat.medianfloatnullableh_distDistribution (min, p16, p25, p50, p75, p84, max) of the 2MASS H magnitude. In ADQL, write h_dist[1] for the minimum, h_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.IR.Hfloatnullablemag_ksRecalibrated 2MASS Ks magnitude.magphot.mag;em.IR.Kfloatnullableerr_ksRecalibrated error from original 2MASS Ks magnitude.magstat.error;phot.mag;em.IR.Kfloatnullableks_bestmultivariate maximum posterior estimate for the 2MASS Ks magnitude.magphot.mag;em.IR.Kfloatnullableks_p50median of the distribution of 2MASS Ks magnitude.magphot.mag;em.IR.K;stat.medianfloatnullableks_distDistribution (min, p16, p25, p50, p75, p84, max) of the 2MASS Ks magnitude. In ADQL, write ks_dist[1] for the minimum, ks_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.IR.Kfloatnullablemag_w1Recalibrated WISE W1 magnitude.magphot.mag;em.IR.3-4umfloatnullableerr_w1Recalibrated error from original WISE W1 magnitude.magstat.error;phot.mag;em.IR.3-4umfloatnullablew1_bestmultivariate maximum posterior estimate for the WISE W1 magnitude.magphot.mag;em.IR.3-4umfloatnullablew1_p50median of the distribution of WISE W1 magnitude.magphot.mag;em.IR.3-4um;stat.medianfloatnullablew1_distDistribution (min, p16, p25, p50, p75, p84, max) of the WISE W1 magnitude. In ADQL, write w1_dist[1] for the minimum, w1_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.IR.3-4umfloatnullablemag_w2Recalibrated WISE W2 magnitude.magphot.mag;em.IR.4-8umfloatnullableerr_w2Recalibrated error from original WISE W2 magnitude.magstat.error;phot.mag;em.IR.4-8umfloatnullablew2_bestmultivariate maximum posterior estimate for the WISE W2 magnitude.magphot.mag;em.IR.4-8umfloatnullablew2_p50median of the distribution of WISE W2 magnitude.magphot.mag;em.IR.4-8um;stat.medianfloatnullablew2_distDistribution (min, p16, p25, p50, p75, p84, max) of the WISE W2 magnitude. In ADQL, write w2_dist[1] for the minimum, w2_dist[2] for the 16th percentile, and so on.magstat;phot.mag;em.IR.4-8umfloatnullabledmod_bestmultivariate maximum posterior estimate for the distance modulus.magphot.mag.distModfloatnullabledmod_p50median of the distribution of distance modulus.magphot.mag.distMod;stat.medianfloatnullabledmod_distDistribution (min, p16, p25, p50, p75, p84, max) of the distance modulus. In ADQL, write dmod_dist[1] for the minimum, dmod_dist[2] for the 16th percentile, and so on.magstat;phot.mag.distModfloatnullablelnlike_bestmultivariate maximum posterior estimate for the log likelihood of the solution (paper Eq. 1).stat.paramfloatnullablelnlike_p50median of the distribution of log likelihood of the solution (paper Eq. 1).stat.param;stat.medianfloatnullablelnlike_distDistribution (min, p16, p25, p50, p75, p84, max) of the log likelihood of the solution (paper Eq. 1). In ADQL, write lnlike_dist[1] for the minimum, lnlike_dist[2] for the 16th percentile, and so on.stat;stat.paramfloatnullablelnp_bestmultivariate maximum posterior estimate for the log posterior of the solution (paper Eq. 2).stat.probabilityfloatnullablelnp_p50median of the distribution of log posterior of the solution (paper Eq. 2).stat.probability;stat.medianfloatnullablelnp_distDistribution (min, p16, p25, p50, p75, p84, max) of the log posterior of the solution (paper Eq. 2). In ADQL, write lnp_dist[1] for the minimum, lnp_dist[2] for the 16th percentile, and so on.stat;stat.probabilityfloatnullablelog10jitter_bestmultivariate maximum posterior estimate for the log photometric likelihood jitter common to all bands.log(mag)stat.paramfloatnullablelog10jitter_p50median of the distribution of log photometric likelihood jitter common to all bands.log(mag)stat.param;stat.medianfloatnullablelog10jitter_distDistribution (min, p16, p25, p50, p75, p84, max) of the log photometric likelihood jitter common to all bands. In ADQL, write log10jitter_dist[1] for the minimum, log10jitter_dist[2] for the 16th percentile, and so on.log(mag)stat;stat.paramfloatnullableparallaxRecalibrated parallax.maspos.parallaxfloatnullableerr_parallaxError in recalibrated parallax.masstat.error;pos.parallaxfloatnullable