V/160ivo://CDS.VizieR/V/160CDSKhalatyan A.Anders F.Chiappini C.Queiroz A.B.A.Nepal S.,dal Ponte M.Jordi C.Guiglion G.Valentini M.Torralba Elipe G.,Steinmetz M.Pantaleoni-Gonzalez M.Malhotra S.Jimenez-Arranz O.,Enke H.Casamiquela L.Ardevol J.2025-08-18T07:36:47Z2024-10-31T15:34:44ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frmilky-way-galaxystellar-distanceeffective-temperatureradial-velocitymetallicityWith Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding ecient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and lineof- sight extinctions from spectro-photometric data. To this end, we built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20mag in V-band extinction, 0.01dex in logarithmic eective temperature, 0.20dex in surface gravity, 0.18dex in metallicity, and 12% in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters AV and Te, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars).2024A&A...691A..98Khttps://cdsarc.cds.unistra.fr/viz-bin/cat/V/160CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toI/352 : Distances to 1.47 billion stars in Gaia EDR3 (Bailer-Jones+, 2021)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=V/160https://vizier.iucaa.in/viz-bin/VizieR-2?-source=V/160http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=V/160https://vizier.cds.unistra.fr/viz-bin/votable?-source=V/160https://vizier.iucaa.in/viz-bin/votable?-source=V/160http://vizieridia.saao.ac.za/viz-bin/votable?-source=V/160GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table V/160/shboost (Data model of the Gaia DR3 SHBoost catalogue)https://vizier.cds.unistra.fr/viz-bin/conesearch/V/160/shboost?https://vizier.iucaa.in/viz-bin/conesearch/V/160/shboost?http://vizieridia.saao.ac.za/viz-bin/conesearch/V/160/shboost?GETtext/xml+votable180.050000true44.99615537864530.00561522634190.0055555555555555560/0-9 11 1/40 42-43 2/165-167 3/656-658 4/2636-2637 2639 5/10552 10554-10555 6/42212-42214https://cdsarc.cds.unistra.fr/viz-bin/moc/V/160?format=asciidefaultV/160/shboostData model of the Gaia DR3 SHBoost catalogueGaiaDR3Gaia DR3 source_id (source_id)meta.id;meta.mainRA_ICRSRight ascension (ICRS) at Ep=2016.0 (ra)degpos.eq.ra;meta.mainDE_ICRSDeclination (ICRS) at Ep=2016 (dec)degpos.eq.dec;meta.mainAVLine-of-sight extinction at {lambda}=5420{AA}, AV, xgboost point estimate (xgb_av)magphys.absorptiondoubleAVmeanLine-of-sight extinction at {lambda}=5420{AA}, AV, xgboost-distribution mean value (xgbdist_av_mean)magphys.absorptiondoubles_AVmeanLine-of-sight extinction at {lambda}=5420{AA}, AV, xgboost-distribution standard deviation (xgbdist_av_std)magphys.absorptiondoublelogTeffEffective temperature, xgboost point estimate (xgb_logteff)log(K)phys.temperature.effectivedoublelogTeffmeanEffective temperature, xgboost-distribution mean value (xgbdist_logteff_mean)log(K)phys.temperature.effective;stat.meandoubles_logTeffmeanEffective temperature, xgboost-distribution standard deviation (xgbdist_logteff_std)log(K)stat.stdev;phys.temperature.effectivedoubleloggSurface gravity, xgboost point estimate (xgb_logg)log(cm.s**-2)phys.gravitydoubleloggmeanSurface gravity, xgboost-distribution mean value (xgbdist_logg_mean)log(cm.s**-2)phys.gravity;stat.meandoubles_loggmeanSurface gravity, xgboost-distribution standard deviation (xgbdist_logg_std)log(cm.s**-2)stat.stdev;phys.gravitydoubleMetMetallicity, xgboost point estimate (xgb_met)phys.abund.ZdoubleMetmeanMetallicity, xgboost-distribution mean value (xgbdist_met_mean)phys.abund.Zdoubles_MetmeanMetallicity, xgboost-distribution standard deviation (xgbdist_met_std)stat.stdev;phys.abund.ZdoubleMassStellar mass, xgboost point estimate (xgb_mass)solMassphys.massdoubleMassmeanStellar mass, xgboost-distribution mean value (xgbdist_mass_mean)solMassphys.massdoubles_MassmeanStellar mass, xgboost-distribution standard deviation (xgbdist_mass_std)solMassstat.stdev;phys.massdoubleDistDistance estimate from the literature (dist)pcpos.distancedoubleb_Dist? 16th distance percentile from the literature (dist_lower)pcpos.distancedoublenullableB_Dist? 84th distance percentile from the literature (dist_upper)pcpos.distancedoublenullablef_Dist[0/2] Distance flag (dist_flag) (1)meta.codeint(BP-RP)0? Dereddened colour, derived with gaia_edr3_photutils (bprp0)magphot.color;em.opt.B;em.opt.RdoublenullableGMAG0Absolute magnitude, derived with gaia_edr3_photutils (mg0)magphys.magAbsdoubleXgalGalactocentric Cartesian X coordinate, derived from dist and assuming X0 = -8.2 kpc (xg)kpcpos.cartesian.x;pos.galactocentricdoubleYgalGalactocentric Cartesian Y coordinate, derived from dist and assuming X0 = 0 kpc (yg)kpcpos.cartesian.y;pos.galactocentricdoubleZgalCartesian Z coordinate, derived from dist and assuming Z0 = 0 (zg)kpcpos.cartesian.z;pos.galacticdoubleRgalGalactocentric planar distance, derived from XGal and YGal (rg)kpcpos.distance;pos.galactocentricdoubleVX? Galactic Cartesian velocity in X direction (vxg)km/sphys.veloc;pos.cartesian.xdoublenullableVY? Galactic Cartesian velocity in Y direction (vyg)km/sphys.veloc;pos.cartesian.ydoublenullableVZ? Galactic Cartesian velocity in Z direction (vzg)km/sphys.veloc;pos.cartesian.zdoublenullableVR? Galactic radial velocity (vrg)km/sspect.dopplerVeloc;pos.galacticdoublenullableVphi? Galactic angular velocity (vphig)km/sphys.veloc.ang;pos.galacticdoublenullableInputFlagSHBoost input flag (xgb_inputflag)meta.codecharq_AV[0/1] AV output quality flag (=0 if xgbdist_av_std<0.3) (xgb_av_outputflag)meta.code.qualintq_logg[0/1] logg output quality flag (=0 if xgbdist_logg_std<0.3) (xgb_logg_outputflag)meta.code.qualintq_logTeff[0/1] logTeff output quality flag (=0 if xgbdist_logteff_std<0.1) (xgb_logteff_outputflag)meta.code.qual;phys.temperature.effectiveintq_Met[0/1] [M/H] output quality flag (=0 if xgbdist_met_std<0.3) (xgb_met_outputflag)meta.code.qualintq_Mass[0/1] Mass output quality flag (=0 if xgbdist_mass_std/xgbdist_mass_mean<0.3) (xgb_mass_outputflag)meta.code.qual;phys.massint