xpparams.mainivo://org.gavo.dc/xpparams/q/mainThe GAVO DC teamZhang, XiangyuGreen, G.M.Rix, Hans-Walter2023-06-16T10:53:27ZGAVO Data Centre Team

Mönchhofstrasse 12-14, D-69120 Heidelberg

gavo@ari.uni-heidelberg.de+49 6221 54 1837stellar-propertiesmilky-way-galaxysky-surveys We present astrophysical parameters of 220 million stars, based on Gaia XP spectra and near-infrared photometry from 2MASS and WISE. Instead of using ab initio stellar models, we develop a data-driven model of Gaia XP spectra as a function of the stellar parameters, with a few straightforward built-in physical assumptions. This resource is a VO re-publication of the resulting catalog of stellar parameters. For bulk downloads, the covariances, the trained model, and more, see https://zenodo.org/record/7811871.2023MNRAS.524.1855Zhttp://dc.g-vo.org/tableinfo/xpparams.mainCatalogResearchIsPreviousVersionOfUnveiling the Milky Way dust extinction curve in 3DGaiaGaia RP/BP Spectrometer0/0-11 6/Opticalxpparams We present astrophysical parameters of 220 million stars, based on Gaia XP spectra and near-infrared photometry from 2MASS and WISE. Instead of using ab initio stellar models, we develop a data-driven model of Gaia XP spectra as a function of the stellar parameters, with a few straightforward built-in physical assumptions. This resource is a VO re-publication of the resulting catalog of stellar parameters. For bulk downloads, the covariances, the trained model, and more, see https://zenodo.org/record/7811871.xpparams.main We present astrophysical parameters of 220 million stars, based on Gaia XP spectra and near-infrared photometry from 2MASS and WISE. Instead of using ab initio stellar models, we develop a data-driven model of Gaia XP spectra as a function of the stellar parameters, with a few straightforward built-in physical assumptions. This resource is a VO re-publication of the resulting catalog of stellar parameters. For bulk downloads, the covariances, the trained model, and more, see https://zenodo.org/record/7811871.220000000source_idGaia DR3 unique source identifier. You can match this against gaia.dr3lite on this TAP service.meta.id;meta.main866939156652803.512553146661156e+174.2899471707395456e+186.706376482488434e+1869175099279225084161.0longindexedprimaryraGaia ICRS right ascension for this object.degpos.eq.ra;meta.main3.96484e-0628.818247.093335.3473601doubleindexednullabledecGaia ICRS declination for this object.degpos.eq.dec;meta.main-89.9887-69.8648-22.841162.735289.97711doubleindexednullableteffEstimated effective Temperature. Note that the raw HDF5 files released by Zhang et al. (2023) give Teff in a different unit (Kilokelvin).Kphys.temperature.effective-787.5943613.845460.627605.1825020.81floatindexednullablefe_hLog of Fe/H in solar unitsphys.abund.Fe-8.66649-1.6993-0.1844590.4151694.369151floatindexednullableloggLog of surface gravity in solar unitsphys.gravity-3.74191.697354.135314.6932119.73271floatindexednullableextEstimated extinction parameter. To convert to the extension at a particular wavelength, multiply this by that wavelength's value in the extinction curve, available at https://zenodo.org/record/7811871/files/extinction_curve.txt?download=1 and in the footnote.magphys.absorption2.06316e-050.02219680.2833251.5368770.36931floatnullablemod_parallaxParallax estimated from the model.maspos.parallax0.008172620.07458310.413052.17084363.5721floatnullableerr_teffError in estimated effective temperature. Note that the raw HDF5 files released by Zhang et al. (2023) give the error in Teff in a different unit (Kilokelvin).Kstat.error;phys.temperature.effective2.8363427.2088101.209487.3039967111floatnullableerr_fe_hError in fe_hstat.error;phys.abund.Fe0.004439960.03100860.1298090.656735994.1421floatnullableerr_loggError in log_gstat.error;phys.gravity0.0008341420.01827640.1241730.736772996.6661floatnullableerr_extError in extmagstat.error;phys.absorption0.001231010.008627230.02595740.0802052981.151floatnullableerr_mod_parallaxError in the parallax estimated from the model.masstat.error;pos.parallax0.0003336220.010240.04462910.111599831.0521floatnullablechi2_optχ² of the best-fit solution. Divide by 61 to obtain χ² per degree of freedom.stat.fit.chi22.7753935.687564.87511221floatnullableln_priorNatural log of the GMM prior on stellar type, at the location of the optimal solution.stat.fit.goodness-444-10.10161.573243.519533.822271floatnullableteff_confidenceA neural-network-based estimate of the confidence in the effective temperature estimate, on a scale of 0 (no confidence) to 1 (high confidence).stat.fit.goodness00.00321960.6630860.98974611floatnullablefeh_confidenceA neural-network-based estimate of the confidence in the [Fe/H] estimate, on a scale of 0 (no confidence) to 1 (high confidence).stat.fit.goodness00.04055790.6933590.98828111floatnullablelogg_confidenceA neural-network-based estimate of the confidence in the log(g) estimate, on a scale of 0 (no confidence) to 1 (high confidence).stat.fit.goodness00.09948730.6723630.97265611floatnullablequality_flagsThe three least significant bits represent whether the confidence in effective temperature, [Fe/H] and log(g) is less than 0.5, respectively. The 4th bit is set if chi2_opt/61 > 2. The 5th bit is set if ln_prior < -7.43. The 6th bit is set if our parallax estimate is more than 10 sigma from the GDR3 measurement (using reported parallax uncertainties from GDR3). The two most significant bits are always unset. We recommend a cut of quality_flags < 8 (the "basic reliability cut"), although a stricter cut of quality_flags == 0 ensures higher reliability at the cost of lower completeness.meta.code.qual00.02.031.0631.0short