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

• Published in: Monthly notices of the Royal Astronomical Society 2024-01, Vol.527 (3), p.7382-7393
• Main Authors: Avdeeva, Aleksandra S, Kovaleva, Dana A, Malkov, Oleg Yu, Zhao, Gang
• Format: Article
• Language: English
• Subjects: Apogees; Astronomical models; Datasets; Estimates; Flags; High resolution; Luminosity; Machine learning; Metallicity; Parameters; Quality assessment; Systematic errors; Temperature
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stad3601

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.