Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra

Abstract

Abstract We derive and publish data-driven estimates of stellar metallicity [M/H] for ∼175 million stars with low-resolution XP spectra published in Gaia DR3. The [M/H] values, along with T eff and log g , are derived using the XGBoost algorithm, trained on stellar parameters from APOGEE, augmented by a set of very-metal-poor stars. XGBoost draws on a number of data features: the full set of XP spectral coefficients, narrowband fluxes derived from XP spectra, and broadband magnitudes. In particular, we include CatWISE magnitudes, as they reduce the degeneracy of T eff and dust reddening. We also include the parallax as a data feature, which helps constrain log g and [M/H]. The resulting mean stellar parameter precision is 0.1 dex in [M/H], 50 K in T eff, and 0.08 dex in log g . This all-sky [M/H] sample is substantially larger than published samples of comparable fidelity across −3 ≲ [M/H] ≲ +0.5. Additionally, we provide a catalog of over 17 million bright (G < 16) red giants whose [M/H] values are vetted to be precise and pure. We present all-sky maps of the Milky Way in different [M/H] regimes that illustrate the purity of the data set, and demonstrate the power of this unprecedented sample to reveal the Milky Way’s structure from its heart to its disk.