J/A+A/664/A38       J-PLUS. Support vector regression              (Wang+, 2022)

J-PLUS: Support vector regression to measure stellar parameters. Wang C., Bai Y., Yuan H., Liu J., Fernandez-Ontiveros J.A., Coelho P.R.T., Jimenez-Esteban F., Galarza C.A., Angulo R.E., Cenarro A.J., Cristobal-Hornillos D., Dupke R.A., Ederoclite A., Hernandez-Monteagudo C., Lopez-Sanjuan C., Marin-Franch A., Moles M., Sodre L.Jr, Vazquez Ramio H., Varela J. <Astron. Astrophys. 664, A38 (2022)> =2022A&A...664A..38W 2022A&A...664A..38W (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Stars, normal ; Effective temperatures ; Abundances, [Fe/H] Keywords: methods: data analysis - techniques: spectroscopic - astronomical databases: miscellaneous Abstract: Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. 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 to efficiently analyse large data sets, such as the one from J-PLUS, and enable 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 parameters regressions is featured with 12-waveband photometry from J-PLUS, and is cross-identified 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 stellar parameters. The method utilizes more than two hundred models to apply the uncertainty analysis. We present a catalog of 2493424 stars with the Root Mean Square Error of 160K 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. Description: We present a catalog of 2493424 stars with the Root Mean Square Error of 160K 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. Table 3 is an example of the interpol.dat, while the extrapolation catalog (extrapol.dat) is not shown in the paper. File Summary: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file interpol.dat 162 2493424 Classification interpolation stars extrapol.dat 162 223924 Classification extrapolation stars -------------------------------------------------------------------------------- See also: J/A+A/643/A149 : J-PLUS Lyα-emitting candidates (Spinoso+, 2020) J/A+A/658/A79 : J-PLUS white dwarf atmospheric parameters (Lopez-Sanjuan+, 2022) J/A+A/659/A144 : J-PLUS. STAR-GALAXY-QSO Classification (Wang+, 2022) J/A+A/659/A181 : J-PLUS DR1 stellar param, and abundances (Yang+, 2022) Byte-by-byte Description of file: extrapol.dat interpol.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 11 A11 --- ID J-PLUS ID (NNNNN-NNNNN) 12 A1 --- n_ID [*] * mean that the parameters are not very reliable 14- 32 F19.15 deg RAdeg Right ascension (J2000.0) 34- 47 F14.11 deg DEdeg Declination (J2000.0) 49- 64 F16.11 K Teff Regressed effective temperature 66- 83 F18.14 K e_Teff Uncertainty of effective temperature 85-101 F17.15 [cm/s2] logg Regressed surface gravity 103-120 F18.16 [cm/s2] e_logg Uncertainty of surface gravity 122-142 E21.18 [-] [Fe/H] Regressed metallicity 144-162 F19.17 [-] e_[Fe/H] Uncertainty of metallicity -------------------------------------------------------------------------------- Acknowledgements: Cunshi Wang, wangcunshi(at)nao.cas.cn
(End) Patricia Vannier [CDS] 05-May-2022
The document above follows the rules of the Standard Description for Astronomical Catalogues; from this documentation it is possible to generate f77 program to load files into arrays or line by line