Applied Machine-Learning Models to Identify Spectral Sub-Types of M Dwarfs from Photometric Surveys

Journal Article · Mon Apr 24 00:00:00 EDT 2023 · Publications of the Astronomical Society of the Pacific

DOI:https://doi.org/10.1088/1538-3873/acc974· OSTI ID:2425105

Sithajan, Sirinrat ^[1]; Meethong, Sukanya ^[1]

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1. National Astronomical Research Institute of Thailand Don Kaeo, Mae Rim, Chiang Mai (Thailand)

M dwarfs are the most abundant stars in the Solar Neighborhood and they are prime targets for searching for rocky planets in habitable zones. Consequently, a detailed characterization of these stars is in demand. The spectral sub-type is one of the parameters that is used for the characterization and it is traditionally derived from the observed spectra. However, obtaining the spectra of M dwarfs is expensive in terms of observation time and resources due to their intrinsic faintness. We study the performance of four machine-learning (ML) models—K-Nearest Neighbor (KNN), Random Forest (RF), Probabilistic Random Forest (PRF), and Multilayer Perceptron (MLP)—in identifying the spectral sub-types of M dwarfs at a grand scale by deploying broadband photometry in the optical and near-infrared. We trained the ML models by using the spectroscopically identified M dwarfs from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 7, together with their photometric colors that were derived from the SDSS, Two-Micron All-Sky Survey, and Wide-field Infrared Survey Explorer. We found that the RF, PRF, and MLP give a comparable prediction accuracy, 74%, while the KNN provides slightly lower accuracy, 71%. We also found that these models can predict the spectral sub-type of M dwarfs with ~99% accuracy within ±1 sub-type. The five most useful features for the prediction are r - z, r - i, r - J, r - H , and g - z, and hence lacking data in all SDSS bands substantially reduces the prediction accuracy. However, we can achieve an accuracy of over 70% when the r and i magnitudes are available. Since the stars in this study are nearby (d ≲ 1300 pc for 95% of the stars), the dust extinction can reduce the prediction accuracy by only 3%. Finally, we used our optimized RF models to predict the spectral sub-types of M dwarfs from the Catalog of Cool Dwarf Targets for the Transiting Exoplanet Survey Satellite, and we provide the optimized RF models for public use.

• Research Organization: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)
• Sponsoring Organization: National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); USDOE
• OSTI ID: 2425105
• Journal Information: Publications of the Astronomical Society of the Pacific, Journal Name: Publications of the Astronomical Society of the Pacific Journal Issue: 1046 Vol. 135; ISSN 0004-6280
• Publisher: Astronomical Society of the Pacific (ASP)
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Astronomy & Astrophysics
Dwarf stars
Late-type stars
Low mass stars
M dwarf stars
Stellar classification
Stellar photometry
Stellar spectral types