文章基本信息

标题：Improve the Search of Very Metal-poor Stars Using the Deep Learning Method
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作者：Jianhang Xie ; Yude Bu ; Junchao Liang 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2021
卷号：162
期号：4
页码：1-10
DOI：10.3847/1538-3881/ac1c7c
语种：English
出版社：American Institute of Physics
摘要：Very metal-poor (VMP) stars have [Fe/H] < −2.0 dex. They are among the oldest stars in the universe, and their unique metallicity can help explore the enrichment mechanism and evolutionary history of the chemical elements of stars in the early universe. However, most current stellar parameter estimation methods do not perform well in determining the stellar parameters of VMP stars, which limits our ability to discover and exploit the properties of VMP stars. In this study, we propose a new model based on a convolutional neural network to determine the stellar atmospheric parameters of VMP stars. We tested our model on the LAMOST spectra; our model can determine the effective temperature (Teff), surface gravity (log g), metallicity ([Fe/H]), and carbon abundance ([C/Fe]) of the LAMOST spectra with precisions σ(Teff) = 134.82 K, = 0.33 dex, σ ([Fe/H]) = 0.20 dex, and σ([C/Fe]) = 0.35 dex. Furthermore, our model can distinguish VMP stars from normal stars with an accuracy of 88.65%. We also compared this model with other widely used methods, and found that this method performs better than other methods. It can be applied to the stellar parameter pipelines of upcoming large surveys such as 4MOST, WEAVES, and MOONS to search for VMP stars and identify carbon-enhanced metal-poor stars.