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Estimation of Stellar Ages and Masses Using Gaussian Process Regression
Bu,Yude1,2; Kumar,Yerra Bharat2; Xie,Jianhang3; Pan,Jingchang3; Zhao,Gang2; Wu,Yaqian2
2020-06-25
Source PublicationThe Astrophysical Journal Supplement Series
ISSN0067-0049
Volume249Issue:1
AbstractAbstract Stellar ages play a crucial role in understanding the formation and evolution of stars and Galaxies, which pose many challenges while determining in practice. In this paper, we have introduced a new machine-learning method, Gaussian process regression (GPR), to estimate the stellar ages, which is different from the traditional isochrone fitting method, which fully utilizes the information provided by previous studies. To demonstrate the performance of our method, we have applied it to the field stars of two important phases of evolution, main-sequence turn-off (MSTO) stars and giants, whose ages and masses are available in the literature. Also, GPR is applied to the red giants of open clusters (e.g., M67). Results showed that the ages given by GPR are in better agreement with those given by isochrone fitting methods. The ages are also estimated from various other machine-learning methods (e.g., support vector regression, neural networks, and random forest) and are compared with GPR, which resulted in GPR outperforming others. In addition to ages, we have applied GPR to estimate the masses of the MSTO stars and red giants and found that the masses predicted by GPR for the red giants are within acceptable uncertainties of masses derived from the asteroseismic scaling relation. We have provided the constraints on the input parameters to GPR, which decides the accuracy of the output ages and masses. Results conclude that the newly introduced GPR is promising to provide a novel approach to estimate stellar ages and masses in the era of big data sets. As a supplement, masses and ages for the MSTO stars and red giants estimated from GPR are provided as a catalog that could be used as a training set for upcoming large data sets with spectroscopic parameters.
KeywordAstronomy data analysis Red giant stars Stellar ages Astronomical methods
DOI10.3847/1538-4365/ab8bcd
Language英语
WOS IDIOP:0067-0049-249-1-ab8bcd
PublisherThe American Astronomical Society
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/63731
Collection中国科学院国家天文台
Affiliation1.School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, People’s Republic of China; buyude@sdu.edu.cn
2.Key Laboratory for Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100012, People’s Republic of China
3.School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, 264209, Shandong, People’s Republic of China
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Bu,Yude,Kumar,Yerra Bharat,Xie,Jianhang,et al. Estimation of Stellar Ages and Masses Using Gaussian Process Regression[J]. The Astrophysical Journal Supplement Series,2020,249(1).
APA Bu,Yude,Kumar,Yerra Bharat,Xie,Jianhang,Pan,Jingchang,Zhao,Gang,&Wu,Yaqian.(2020).Estimation of Stellar Ages and Masses Using Gaussian Process Regression.The Astrophysical Journal Supplement Series,249(1).
MLA Bu,Yude,et al."Estimation of Stellar Ages and Masses Using Gaussian Process Regression".The Astrophysical Journal Supplement Series 249.1(2020).
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