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.

根据各种估计，褐矮星 (BD) 应该占银河系所有天体的 25%。然而，无论是作为个体还是作为一个群体，其中很少有人被发现并得到充分研究。此类研究需要均匀且完整的褐矮星样本。由于褐矮星的弱点，对它们的光谱研究相当费力。因此，通过光谱观测证实，创建一个重要且可靠的褐矮星样本目前似乎是不可能实现的。人们已经进行了许多尝试来寻找和创建一组褐矮星，利用它们的颜色作为应用于大量调查数据的决策规则。在这项工作中，我们在 PanStarrs DR1 上使用随机森林分类器、XGBoost、SVM 分类器和 TabNet 等机器学习方法，2MASS 和 WISE 数据可将 L 和 T 棕矮星与其他光谱和光度类别的天体区分开来。讨论了模型的解释。我们还将我们的模型与经典决策规则进行比较，证明它们的效率和相关性。