Machine learning methods for the search for L&T brown dwarfs in the data of modern sky surveys

TL;DR

This paper applies machine learning algorithms to large sky survey datasets to efficiently identify L and T brown dwarfs, aiming to improve sample completeness and reliability for astrophysical studies.

Contribution

It introduces the use of multiple machine learning models for brown dwarf classification and compares their performance with classical decision rules.

Findings

Machine learning models outperform classical decision rules in brown dwarf identification.

Models achieve high accuracy in distinguishing brown dwarfs from other objects.

The study discusses model interpretability and relevance for future surveys.

Abstract

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.