Estimation of Stellar Ages and Masses Using Gaussian Process Regression
Estimation of Stellar Ages and Masses Using Gaussian Process Regression
复制标题
使用高斯过程回归估计恒星的年龄和质量
DOI：
10.3847/1538-4365/ab8bcd
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发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Wu Yaiqian
中科院分区：
文献类型：
--
作者：
Bu Yude;Yerra Bharat Kumar;Xie Jianhang;Pan Jingchang;Zhao Gang;Wu Yaiqian
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.
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DOI：
10.1086/300810
发表时间：
1999-01
期刊：
The Astronomical Journal
影响因子：
--
作者：
B. Twarog;B. Anthony-Twarog;Andrew R. Bricker
通讯作者：
B. Twarog;B. Anthony-Twarog;Andrew R. Bricker
DOI：
10.1117/12.2234208
发表时间：
2016-06
期刊：
--
影响因子：
--
作者：
W. Saunders;P. Gillingham
通讯作者：
W. Saunders;P. Gillingham
DOI：
10.1088/0004-637x/798/2/122
发表时间：
2014-11
期刊：
The Astrophysical Journal
影响因子：
--
作者：
A. Miller;J. Bloom;J. Richards;Y. Lee;D. Starr;N. Butler;S. Tokarz;N. Smith;J. Eisner;JPLCaltech;U. Berkeley;Lbnl;wise.io;Chungnam National University;A. S. University;Sao;U. Arizona
通讯作者：
A. Miller;J. Bloom;J. Richards;Y. Lee;D. Starr;N. Butler;S. Tokarz;N. Smith;J. Eisner;JPLCaltech;U. Berkeley;Lbnl;wise.io;Chungnam National University;A. S. University;Sao;U. Arizona
影响因子：
4.8
作者：
J. Mackereth;J. Bovy;R. Schiavon;G. Zasowski;K. Cunha;P. Frinchaboy;A. G. Pérez;M. Hayden;
通讯作者：
J. Mackereth;J. Bovy;R. Schiavon;G. Zasowski;K. Cunha;P. Frinchaboy;A. G. Pérez;M. Hayden;
影响因子：
6.5
作者：
B. R. Jørgensen;L. Lindegren
通讯作者：
B. R. Jørgensen;L. Lindegren