Applied Machine-Learning Models to Identify Spectral Sub-Types of M Dwarfs from Photometric Surveys
Abstract
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.
- Publication:
-
Publications of the Astronomical Society of the Pacific
- Pub Date:
- April 2023
- DOI:
- 10.1088/1538-3873/acc974
- arXiv:
- arXiv:2304.14113
- Bibcode:
- 2023PASP..135d4502S
- Keywords:
-
- Stellar classification;
- Stellar photometry;
- Dwarf stars;
- Late-type stars;
- Low mass stars;
- M dwarf stars;
- Stellar spectral types;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Solar and Stellar Astrophysics
- E-Print:
- 17 pages, 7 figures, 6 tables, Accepted for publication in PASP