J-PLUS: Support vector regression to measure stellar parameters^★

¹ Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012, PR China
e-mail: wangcunshi@nao.cas.cn
² College of Astronomy and Space Sciences, University of Chinese Academy of Sciences, Beijing 100049, PR China
³ Department of Astronomy, Beijing Normal University, Beijing 100875, PR China
⁴ Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Unidad Asociada al CSIC, Plaza San Juan 1, 44001 Teruel, Spain
⁵ Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, 05508-090 São Paulo, Brazil
⁶ Departamento de Astrofísica, Centro de Astrobiología (CSIC-INTA), ESAC Campus, Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Madrid, Spain
⁷ Observatório Nacional – MCTI (ON), Rua Gal. José Cristino 77, São Cristóvão, 20921-400 Rio de Janeiro, Brazil
⁸ Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizabal 4, 20018 Donostia-San Sebastián, Spain
⁹ IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
¹⁰ Department of Astronomy, University of Michigan, 1085 South University Ave., Ann Arbor, MI 48109, USA
¹¹ Department of Physics and Astronomy, University of Alabama, Gallalee Hall, Tuscaloosa, AL 35401, USA
¹² Instituto de Astrofísica de Canarias, La Laguna, 38205 Tenerife, Spain
¹³ Departamento de Astrofísica, Universidad de La Laguna, 38206 Tenerife, Spain

Received: 17 January 2022
Accepted: 29 April 2022

Abstract

Context. 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.

Aims. 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.

Methods. The training data for the parameters 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.

Results. 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.