J-PLUS: Support vector regression to measure stellar parameters

被引:6
|
作者
Wang, C. [1 ,2 ]
Bai, Y. [1 ]
Yuan, H. [3 ]
Liu, J. [1 ,2 ]
Fernandez-Ontiveros, J. A. [4 ]
Coelho, P. R. T. [5 ]
Jimenez-Esteban, F. [6 ]
Galarza, C. A. [7 ]
Angulo, R. E. [8 ,9 ]
Cenarro, A. J. [4 ]
Cristobal-Hornillos, D. [4 ]
Dupke, R. A. [7 ,10 ,11 ]
Ederoclite, A. [4 ]
Hernandez-Monteagudo, C. [12 ,13 ]
Lopez-Sanjuan, C. [4 ]
Marin-Franch, A. [4 ]
Moles, M. [4 ]
Sodre, L., Jr. [5 ]
Ramio, H. Vazquez [4 ]
Varela, J. [4 ]
机构
[1] Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, 20A Datun Rd, Beijing 100012, Peoples R China
[2] Univ Chinese Acad Sci, Coll Astron & Space Sci, Beijing 100049, Peoples R China
[3] Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China
[4] Ctr Estudios Fis Cosmos Aragon CEFCA, Unidad Asociada CSIC, Plaza San Juan 1, Teruel 44001, Spain
[5] Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, BR-05508090 Sao Paulo, Brazil
[6] Ctr Astrobiol CSIC INTA, Dept Astrofis, ESAC Campus,Camino Bajo Castillo S-N, Madrid, Spain
[7] Observ Nacl MCTI ON, Rua Gal Jose Cristino 77, BR-20921400 Rio De Janeiro, Brazil
[8] Donostia Int Phys Ctr DIPC, Paseo Manuel de Lardizabal 4, Donostia San Sebastian 20018, Spain
[9] Basque Fdn Sci, Ikerbasque, Bilbao 48013, Spain
[10] Univ Michigan, Dept Astron, 1085 South Univ Ave, Ann Arbor, MI 48109 USA
[11] Univ Alabama, Dept Phys & Astron, Gallalee Hall, Tuscaloosa, AL 35401 USA
[12] Inst Astrofis Canarias, San Cristobal la Laguna, Spain
[13] Univ La Laguna, Dept Astrofis, Tenerife 38206, Spain
基金
美国安德鲁·梅隆基金会; 中国国家自然科学基金;
关键词
methods: data analysis; techniques: spectroscopic; astronomical databases: miscellaneous; ATMOSPHERIC PARAMETERS; SEGUE; SPECTRA; APOGEE; STARS;
D O I
10.1051/0004-6361/202243130
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
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.
引用
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页数:15
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