Spectral classification of brown dwarfs using machine learning

TL;DR

This study employs machine learning algorithms, specifically Random Forest and Gaussian Processes, to classify brown dwarf spectral types using photometric data, achieving high accuracy and applying the models to unclassified objects.

Contribution

The paper introduces a novel approach using machine learning to estimate brown dwarf spectral types solely from photometric data, demonstrating effectiveness with real data.

Findings

Both models achieved F1-scores above 0.8 on the test set.

Applied models to 21 brown dwarfs, identifying their spectral types.

Machine learning with multi-band photometry effectively classifies brown dwarfs.

Abstract

Brown dwarfs are compact objects that do not reach temperatures high enough to produce sustained hydrogen fusion. Consequently, they cool over time, gradually evolving through later spectral types. In fact, three new spectral types (L, T, and Y) were added to the Harvard sequence to accommodate the spectral features of brown dwarfs. During the cooling process, some brown dwarfs unexpectedly become bluer instead of redder (at optical and near-infrared wavelengths). This phenomenon, known as the bluing effect, is particularly noticeable at the L/T spectral transition. The aim of this work is to approximate the spectral type of brown dwarfs using only photometric data, in particular 2MASS and WISE magnitudes. We used two machine learning algorithms, Random Forest and Gaussian Processes, which were evaluated using a 70/30 train/test split. Both models were trained using 5-fold cross-validation and achieved F1-scores of 0.84 and 0.87, respectively, on the test set. After validating the reliability of the algorithms, we applied them to 21 isolated brown dwarfs without prior spectral type determinations. Our results indicate that 5 of these objects have a spectral type between L0 and L4, while the remaining 16 fall within the M6-M9 range. Machine learning algorithms, combined with multi-band photometry, are a powerful tool for estimating the spectral types of brown dwarfs.