分类： 天文学 >> 天文学 提交时间： 2023-02-19
Cunshi Wang
Yu Bai
Haibo Yuan
Jifeng Liu
J. A. Fernández-Ontiveros
Paula R. T. Coelho
F. Jiménez-Esteban
Carlos Andrés Galarza
R. E. Angulo
A. J. Cenarro
D. Cristóbal-Hornillos
R. A. Dupke
A. Ederoclite
C. Hernández-Monteagudo
C. López-Sanjuan
A. Marín-Franch
M. Moles
L. Sodré
H. Vázquez Ramió
J. Varela
摘要： Context. 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 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. Aims. The main goal of this study is to construct a 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 is featured with 12-waveband photometry from J-PLUS, and is cross-identified with spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and the SEGUE. 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. Results. We present a catalog of 2,493,424 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.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Lin Yang
Haibo Yuan
Maosheng Xiang
Fuqing Duan
Yang Huang
Jifeng Liu
Timothy C. Beers
Carlos Andrés Galarza
Simone Daflon
J. A. Fernández-Ontiveros
Javier Cenarro
David Cristóbal-Hornillos
Carlos Hernández-Monteagudo
Carlos López-Sanjuan
Antonio Marín-Franch
Mariano Moles
Jesús Varela
Héctor Vázquez Ramió
Jailson Alcaniz
Renato Dupke
摘要： Context. The Javalambre Photometric Local Universe Survey (J-PLUS) has obtained precise photometry in twelve specially designed filters for large numbers of Galactic stars. Deriving their precise stellar atmospheric parameters and individual elemental abundances is crucial for studies of Galactic structure, and the assembly history and chemical evolution of our Galaxy. Aims. Our goal is to estimate not only stellar parameters (effective temperature, Teff, surface gravity, log g, and metallicity, [Fe/H]), but also [{\alpha}/Fe] and four elemental abundances ([C/Fe], [N/Fe], [Mg/Fe], and [Ca/Fe]) using data from J-PLUS DR1. Methods. By combining recalibrated photometric data from J-PLUS DR1, Gaia DR2, and spectroscopic labels from LAMOST, we design and train a set of cost-sensitive neural networks, the CSNet, to learn the non-linear mapping from stellar colors to their labels. Results. We have achieved precisions of {\delta}Teff {\sim}55K, {\delta}logg{\sim}0.15dex, and {\delta}[Fe/H]{\sim}0.07dex, respectively, over a wide range of temperature, surface gravity, and metallicity. The uncertainties of the abundance estimates for [{\alpha}/Fe] and the four individual elements are in the range 0.04-0.08 dex. We compare our parameter and abundance estimates with those from other spectroscopic catalogs such as APOGEE and GALAH, and find an overall good agreement. Conclusions. Our results demonstrate the potential of well-designed, high-quality photometric data for determinations of stellar parameters as well as individual elemental abundances. Applying the method to J-PLUS DR1, we have obtained the aforementioned parameters for about two million stars, providing an outstanding data set for chemo-dynamic analyses of the Milky Way. The catalog of the estimated parameters is publicly accessible.
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