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.

翻译：根据不同估算，褐矮星在银河系所有天体中占比应高达25%。然而，目前仅有少数褐矮星被探测到并得到充分研究——无论是作为独立个体还是作为群体进行研究。此类研究需要均匀且完备的褐矮星样本。由于其暗弱性，褐矮星的光谱研究相当耗时。正因如此，目前看来，构建一个经光谱观测确认的可靠褐矮星大样本似乎难以实现。已有大量尝试通过将褐矮星的颜色特征作为决策规则，应用于海量巡天数据来搜寻并建立褐矮星集合。在本研究中，我们利用PanStarrs DR1、2MASS和WISE数据，采用随机森林分类器、XGBoost、支持向量机分类器和TabNet等机器学习方法，将L型和T型褐矮星与其他光谱类型及光度类型的天体区分开来。我们探讨了模型的可解释性，并将我们的模型与传统决策规则进行比较，证明了其高效性与适用性。