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

Abstract: We derive and publish data-driven estimates of stellar metallicities [M/H] for 175 million stars with low-resolution XP spectra published in Gaia DR3. The [M/H] values, along with Teff and logg, 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 Teff and dust reddening. We also include the parallax as a data feature, which helps constrain logg and [M/H]. The resulting mean stellar parameter precision is 0.1 dex in [M/H], 50 K in Teff, and 0.08 dex in logg. 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] 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 dataset, and demonstrate the power of this unprecedented sample to reveal the Milky Way's structure from its heart to its disk.