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The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse setof astrometric, photometric, and spectroscopic measurements for more than abillion stars. The wealth and complexity of the data makes traditionalapproaches for estimating stellar parameters for the full Gaia dataset almostprohibitive. We have explored different supervised learning methods forextracting basic stellar parameters as well as distances and line-of-sightextinctions, given spectro-photo-astrometric data (including also the new GaiaXP spectra). For training we use an enhanced high-quality dataset compiled fromGaia DR3 and ground-based spectroscopic survey data covering the whole sky andall Galactic components. We show that even with a simple neural-networkarchitecture or tree-based algorithm (and in the absence of Gaia XP spectra),we succeed in predicting competitive results (compared to Bayesian isochronefitting) down to faint magnitudes. We will present a new Gaia DR3stellar-parameter catalogue obtained using the currently best-performingmachine-learning algorithm for tabular data, XGBoost, in the near future.

Parameters for > 300 million Gaia stars: Bayesian inference vs. machine learning