Brown dwarfs are intermediate objects between stars and planets. Their mass is not enough to start and maintain stable hydrogen fusion, which causes them to cool over time. Exploration of brown dwarfs is interesting for several reasons. First, the transition boundary between planets and brown dwarfs is not fully understood. Second, atmospheric properties of brown dwarfs strongly affect their photometry, which can not be fully explain yet by modern atmospherical models.
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 by now. Numerous attempts have been made to search for and create a set of brown dwarfs using their colors as a decision rule applied to a vast amount of surveys' data. In this work we use Random Forest Classifier, XGBoost, SVM Classifier and TabNet on PanStarrs DR1, 2MASS and WISE data to distinguish L and T type of brown dwarfs from objects of other spectral and luminosity classes. We also compare our models with classical decision rule models, proving their efficiency and relevance.