Spectral Typing with Artificial Intelligence: Classifying Low-resolution Near-infrared Spectra of Standard M/L/T Dwarfs

Abstract

We investigate the application of supervised machine learning models to directly infer the spectral types of ultracool dwarfs (dwarf spectral types ≥M6) using binned fluxes as feature labels. We compare the ability of two machine learning frameworks, k-Nearest Neighbor (kNN) and Random Forest (RF), to classify low-resolution near-infrared spectra of M6 to T9 dwarfs (3100 K ≳ T eff ≳ 500 K). We used a synthetic training data set of 2400 spectra generated from 24 spectral type standards and validated our models on 315 spectra with previous literature classifications. Classification accuracies within ± 1 subtype were 98.4% ± 0.7% for the kNN model and 95.6% ± 1.2% for the RF model, indicating the kNN performs marginally better for spectral-type estimation. Future studies will explore a broader range of stellar properties such as metallicity, gravity, and cloud characteristics and additional machine learning models.