J-PLUS: Support vector regression to measure stellar parameters

Journal Article · 04 August 2022 · Astronomy and Astrophysics

DOI: https://doi.org/10.1051/0004-6361/202243130 · OSTI ID:1982332

Wang, Cunshi ^[1]; Bai, Y. ^[2]; Yuan, H. ^[3]; Liu, J. ^[1]; Fernández-Ontiveros, J. A. ^[4]; Coelho, P. R. T. ^[5]; Jiménez-Esteban, F. ^[6]; Galarza, C. A. ^[7]; Angulo, R. E. ^[8]; Cenarro, A. J. ^[4]; Cristóbal-Hornillos, D. ^[4]; Dupke, R. A. ^[9]; Ederoclite, A. ^[4]; Hernández-Monteagudo, C. ^[10]; López-Sanjuan, C. ^[4]; Marín-Franch, A. ^[4]; Moles, M. ^[4]; Sodré, Jr., L. ^[5]; Ramió, H. Vázquez ^[4]; Varela, J. ^[4]

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1. Chinese Academy of Sciences (CAS), Beijing (China); University of Chinese Academy of Sciences, Beijing (China)
2. Chinese Academy of Sciences (CAS), Beijing (China)
3. Beijing Normal University (China)
4. Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Teruel (Spain)
5. Universidade de São Paulo (Brazil)
6. European Space Astronomy Centre (ESAC), Madrid (Spain)
7. Observatório Nacional (ON), Rio de Janeiro (Brazil)
8. Donostia International Physics Centre (DIPC), San Sebastián (Spain); Basque Foundation for Science, Bilbao (Spain). IKERBASQUE
9. Observatório Nacional (ON), Rio de Janeiro (Brazil); University of Michigan, Ann Arbor, MI (United States); University of Alabama, Tuscaloosa, AL (United States)
10. Instituto de Astrofísica de Canarias, La Laguna (Spain); Universidad de La Laguna (Spain)

Stellar parameters are among the most important characteristics in studies of stars which, in traditional methods, are based on atmosphere models. However, time, cost, and brightness limits restrain the efficiency of spectral observations. The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools for efficiently analyzing large data sets, such as the one from J-PLUS, and enables us to expand the research domain to stellar parameters. The main goal of this study is to construct a support vector regression (SVR) algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. The training data for the parameter's regressions are featured with 12-waveband photometry from J-PLUS and are crossidentified with spectrum-based catalogs. These catalogs are from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Apache Point Observatory Galactic Evolution Experiment, and the Sloan Extension for Galactic Understanding and Exploration. We then label them with the stellar effective temperature, the surface gravity, and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach, in order to fully take into account, the uncertainties of both the magnitudes and the stellar parameters. The method utilizes more than 200 models to apply the uncertainty analysis. We present a catalog of 2 493 424 stars with the root mean square error of 160 K in the effective temperature regression, 0.35 in the surface gravity regression, and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.

• Research Organization: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)
• Sponsoring Organization: National Natural Science Foundation of China (NSFC); Spanish Ministry of Economy and Competitiveness (MINECO); Spanish Ministry of Science, Innovation and Universities (MCIU); USDOE Office of Science (SC)
• OSTI ID: 1982332
• Journal Information: Astronomy and Astrophysics, Journal Name: Astronomy and Astrophysics Vol. 664; ISSN 0004-6361
• Publisher: EDP SciencesCopyright Statement
• Country of Publication: United States
• Language: English