Quality flags for GSP-Phot Gaia DR3 astrophysical parameters with machine learning: Effective temperatures case study
| Autor: | Avdeeva, Aleksandra S., Kovaleva, Dana A., Malkov, Oleg Yu., Zhao, Gang |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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 datasets, 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. Consequently, the developed models offer a valuable quality assessment tool for GSP-Phot effective temperatures in Gaia DR3. The dataset with flags for all GSP-Phot effective temperature estimates, is publicly available, as are the models themselves. Comment: 13 pages, 10 figures |
| Databáze: | arXiv |
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