J-PLUS: Support vector machine applied to STAR-GALAXY-QSO classification^⋆

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 Department of Physics, Lancaster University, Lancaster LA1 4YB, UK
⁵ PPGFis & Núcleo de Astrofísica e Cosmologia (Cosmo-ufes), Universidade Federal do Espírito Santo, 29075-910 Vitória, ES, Brazil
⁶ Departamento de Astrofísica, Universidad de La Laguna (ULL), 38206 La Laguna, Tenerife, Spain
⁷ Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain
⁸ Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Unidad Asociada al CSIC, Plaza San Juan 1, 44001 Teruel, Spain
⁹ Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, 05508-090 São Paulo, Brazil
¹⁰ Observatório Nacional – MCTI (ON), Rua Gal. José Cristino 77, São Cristóvão, 20921-400 Rio de Janeiro, Brazil
¹¹ Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizabal 4, 20018 Donostia-San Sebastián, Spain
¹² IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
¹³ University of Michigan, Department of Astronomy, 1085 South University Ave., Ann Arbor, MI 48109, USA
¹⁴ University of Alabama, Department of Physics and Astronomy, Gallalee Hall, Tuscaloosa, AL 35401, USA
¹⁵ Instituto de Astrofísica de Canarias, La Laguna, 38205 Tenerife, Spain

Received: 19 September 2021
Accepted: 13 December 2021

Abstract

Context. In modern astronomy, machine learning has proved to be efficient and effective in mining big data from the newest telescopes.

Aims. In this study, we construct a supervised machine-learning algorithm to classify the objects in the Javalambre Photometric Local Universe Survey first data release (J-PLUS DR1).

Methods. The sample set is featured with 12-waveband photometry and labeled with spectrum-based catalogs, including Sloan Digital Sky Survey spectroscopic data, the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, and VERONCAT – the Veron Catalog of Quasars & AGN. The performance of the classifier is presented with the applications of blind test validations based on RAdial Velocity Extension, the Kepler Input Catalog, the Two Micron All Sky Survey Redshift Survey, and the UV-bright Quasar Survey. A new algorithm was applied to constrain the potential extrapolation that could decrease the performance of the machine-learning classifier.

Results. The accuracies of the classifier are 96.5% in the blind test and 97.0% in training cross-validation. The F₁-scores for each class are presented to show the balance between the precision and the recall of the classifier. We also discuss different methods to constrain the potential extrapolation.