J/AJ/158/93ivo://CDS.VizieR/J/AJ/158/93doi:10.26093/cds/vizier.51580093CDSBai Y.Liu J.Bai Z.Wang S.Fan D.2024-01-09T13:03:59Z2019-10-21T13:38:43ZCDS support team

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

cds-question@unistra.freffective-temperatureThis paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second Gaia data release (Cat. I/345), based on the combination of the stars in four spectroscopic surveys: the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for Galactic Understanding and Exploration, the Apache Point Observatory Galactic Evolution Experiment, and the Radial Velocity Extension. This combination, of about four million stars, enables us to construct one of the largest training samples for the regression and further predict reliable stellar temperatures with a rms error of 191 K. This result is more precise than that given by the Gaia second data release that is based on about sixty thousands stars. After a series of data cleaning processes, the input features that feed the regressor are carefully selected from the Gaia parameters, including the colors, the 3D position, and the proper motion. These Gaia parameters are used to predict effective temperatures for 132739323 valid stars in the second Gaia data release. We also present a new method for blind tests and a test for external regression without additional data. The machine-learning algorithm fed with the parameters only in one catalog provides us with an effective approach to maximize the sample size for prediction, and this methodology has a wide application prospect in future studies of astrophysics.2019AJ....158...93Bhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/AJ/158/93CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toI/345 : Gaia DR2 (Gaia Collaboration, 2018)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/AJ/158/93https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/AJ/158/93http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/AJ/158/93https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/AJ/158/93https://vizier.iucaa.in/viz-bin/votable?-source=J/AJ/158/93http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/AJ/158/93GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/AJ/158/93/table2 (Results of our regression in Gaia DR2 (Cat. I/345))https://vizier.cds.unistra.fr/viz-bin/conesearch/J/AJ/158/93/table2?https://vizier.iucaa.in/viz-bin/conesearch/J/AJ/158/93/table2?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/AJ/158/93/table2?GETtext/xml+votable180.050000true45.00431616420760.02104503268970.0055555555555555560/0-11 6/https://cdsarc.cds.unistra.fr/viz-bin/moc/J/AJ/158/93?format=asciidefaultJ/AJ/158/93/table2Results of our regression in Gaia DR2 (Cat. I/345)SourceSource identifier in Gaia DR2 (Gaia)meta.id;meta.mainindexedRA_ICRSGaia DR2 Right Ascension (ICRS) at Ep=2015.5 (column added by the CDS)degpos.eq.ra;meta.mainDE_ICRSGaia DR2 Declination (ICRS) at Ep=2015.5 (column added by the CDS)degpos.eq.dec;meta.mainTeff[3893/8871] Regressed effective temperature (Teff)Kphys.temperature.effectiveinte_Teff[9/1098] Uncertainty in Teff (e_Teff)Kstat.error;phys.temperature.effectiveint