Identifying and Characterizing Very Low Mass Spectral Blend Binaries with Machine Learning Methods

TL;DR

This paper develops machine learning models, specifically hierarchical random forests, to identify and characterize unresolved very low mass spectral binary systems, outperforming traditional index-based methods in accuracy and speed.

Contribution

The authors introduce a novel machine learning approach using hierarchical random forests for spectral binary identification and component classification, with improved performance over existing methods.

Findings

Models achieve >85% recall and precision in binary identification.

Median component classification errors are less than 0.1 subtypes.

Performance degrades with similar component types but remains high in optimal ranges.

Abstract

We present an approach to identifying and characterizing unresolved, very low mass spectral blend binaries composed of late-M, L, and T dwarfs using machine learning methodologies. We generated and evaluated a series of hierarchical random forest models to distinguish spectral blends from single very low-mass dwarfs, and to classify their primary and secondary components. Models were trained on a sample of single and synthesized binary templates generated from empirical spectra. We explored various aspects of the design of our models, and find that models trained on a full range of single and binary combinations have the best performance for identification and component classification. These models achieve binary identification recall and precision of $\gtrsim$85%, median component classification errors of $\lesssim$0.1 subtypes, and systematic classification uncertainties of $\lesssim$1 subtype, outperforming index-based methods in terms of fidelity, range, and speed. Optimal performance is achieved for binaries composed of L and T dwarf primaries and late-L and T dwarf secondaries. When applied to the spectra of previously confirmed very low-mass binaries, model performance is degraded due to the prevalence of systems with similar component types, but remains high in the optimal performance range. We propose potential improvements to these models, which can be used to explore binary populations among the thousands to millions of very low-mass stars and brown dwarfs anticipated with large-scale spectral surveys such as SPHEREx and Euclid.