Exploiting the full Gaia data: From Bayesian isochrone fitting to transferring spectroscopic stellar parameters to 1 billion Gaia DR3 stars
Abstract
I will present a new stellar-parameter catalogue obtained from Bayesian isochrone fitting for 400 million Gaia EDR3 stars (cross-matched with the large-scale photometric surveys SkyMapper, PanSTARRS, 2MASS, AllWISE). The upcoming Gaia Data Release 3 (DR3, to be published in June 2022) will deliver an even diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars. The wealth and complexity of the new data makes traditional approaches for estimating stellar parameters for the full Gaia dataset prohibitive. We therefore explore different supervised learning methods for extracting basic stellar parameters as well as distances and line-of-sight extinctions, given spectro-photo-astrometric data (including also the new Gaia XP spectra). For training we will use an enhanced high-quality dataset compiled from Gaia DR3 and ground-based spectroscopic survey data covering the whole sky and all Galactic components. I will show some first results (based on Gaia DR2) that even with a simple neural-network architecture and in the absence of Gaia XP spectra, one can succeed in predicting competitive results down to faint magnitudes.