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.

利用机器学习方法，如随机森林分类器、XGBoost、支持向量机分类器和TabNet，基于PanStarrs DR1、2MASS和WISE数据，区分L和T型棕矮星与其他光谱和亮度类别的物体，并对模型进行解释。同时，我们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。

现代天文巡天数据中寻找L&T类褐矮星的机器学习方法