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Gaia Data Release 3 (DR3) provides extensive information on the astrophysicalproperties of stars, such as effective temperature, surface gravity,metallicity, and luminosity, for over 470 million objects. However, as Gaia'sstellar parameters in GSP-Phot module are derived through model-dependentmethods and indirect measurements, it can lead to additional systematic errorsin the derived parameters. In this study, we compare GSP-Phot effectivetemperature estimates with two high-resolution and high signal-to-noisespectroscopic catalogues: APOGEE DR17 and GALAH DR3, aiming to assess thereliability of Gaia's temperatures. We introduce an approach to distinguishgood-quality Gaia DR3 effective temperatures using machine-learning methodssuch as XGBoost, CatBoost and LightGBM. The models create quality flags, whichcan help one to distinguish good-quality GSP-Phot effective temperatures. Wetest our models on three independent datasets, including PASTEL, a compilationof spectroscopically derived stellar parameters from different high-resolutionstudies. The results of the test suggest that with these models it is possibleto filter effective temperatures as accurate as 250 K with ~ 90 per centprecision even in complex regions, such as the Galactic plane. Consequently,the models developed herein offer a valuable quality assessment tool forGSP-Phot effective temperatures in Gaia DR3. Consequently, the developed modelsoffer a valuable quality assessment tool for GSP-Phot effective temperatures inGaia DR3. The dataset with flags for all GSP-Phot effective temperatureestimates, is publicly available, as are the models themselves.

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