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Despite having different astronomical characteristics, the studies of mira variables and ultra-cool dwarfs frequently show similar red colors, which could cause leading to photometric misclassification. This study uses photometric data from the WISE, 2MASS, and Pan-STARRS surveys to construct color-based selection criteria for red dwarfs, brown dwarfs, and Mira variables. On analyzing the color indices, we developed empirical rules that separate these objects with an overall classification accuracy of approximately 91%-92%. While the differentiation between red dwarfs and both Mira variables and brown dwarfs is effective, challenges remain in distinguishing Mira variables from brown dwarfs due to overlapping color indices. The robustness of our classification technique was validated by a bootstrap analysis, highlighting the significance of color indices in large photometric surveys for stellar classification., Comment: Accepted to Peremennye Zvezdy (Variable Stars), comments are welcomed

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

According to various estimates, brown dwarfs (BD) should account for up to 25 percent of all objects in the Galaxy. However, few of them are discovered and well-studied, both individually and as a population. Homogeneous and complete samples of brown dwarfs are needed for these kinds of studies. Due to their weakness, spectral studies of brown dwarfs are rather laborious. For this reason, creating a significant reliable sample of brown dwarfs, confirmed by spectroscopic observations, seems unattainable at the moment. Numerous attempts have been made to search for and create a set of brown dwarfs using their colours as a decision rule applied to a vast amount of survey data. In this work, we use machine learning methods such as Random Forest Classifier, XGBoost, SVM Classifier and TabNet on PanStarrs DR1, 2MASS and WISE data to distinguish L and T brown dwarfs from objects of other spectral and luminosity classes. The explanation of the models is discussed. We also compare our models with classical decision rules, proving their efficiency and relevance., Comment: 12 pages, 10 figures, Accepted for publication in Astronomy and Computing

We determine the interstellar extinction in the selected high-latitude areas of the sky based on Gaia EDR3 astrometry and photometry and spectroscopic data from RAVE survey. We approximate the results with the cosecant law in each area thus deriving the parameters of the barometric formula for different lines of sight. The distribution of the parameters over the entire sky is described using spherical harmonics. As a result, we get a mathematical description of the interstellar visual extinction for different lines of sight and distances from the Sun which can be used for estimating interstellar extinction., Comment: 11 pages, 6 figures