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