Quality flags for GSP-Phot Gaia DR3 astrophysical parameters with machine learning: effective temperatures case study

Abstract

ABSTRACT Gaia Data Release 3 (DR3) provides extensive information on the astrophysical properties of stars, such as effective temperature, surface gravity, metallicity, and luminosity, for over 470 million objects. However, as Gaia’s stellar parameters in GSP-Phot module are derived through model-dependent methods and indirect measurements, it can lead to additional systematic errors in the derived parameters. In this study, we compare GSP-Phot effective temperature estimates with two high-resolution and high signal-to-noise spectroscopic catalogues: APOGEE DR17 and GALAH DR3, aiming to assess the reliability of Gaia’s temperatures. We introduce an approach to distinguish good-quality Gaia DR3 effective temperatures using machine-learning methods such as XGBoost, CatBoost, and LightGBM. The models create quality flags, which can help one to distinguish good-quality GSP-Phot effective temperatures. We test our models on three independent data sets, including PASTEL, a compilation of spectroscopically derived stellar parameters from different high-resolution studies. The results of the test suggest that with these models, it is possible to filter effective temperatures as accurate as 250 K with ∼90 per cent precision even in complex regions, such as the Galactic plane. Consequently, the models developed herein offer a valuable quality assessment tool for GSP-Phot effective temperatures in Gaia DR3. The data set with flags for all GSP-Phot effective temperature estimates, is publicly available, as are the models themselves.