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

Author:

Zhou TianxingORCID,Theissen Christopher A.ORCID,Burgasser Adam J.ORCID,Best William M. J.ORCID,Feeser S. JeanORCID

Abstract

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.

Publisher

American Astronomical Society

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