Improve the Search of Very Metal-poor Stars Using the Deep Learning Method
Improve the Search of Very Metal-poor Stars Using the Deep Learning Method
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DOI：
10.3847/1538-3881/ac1c7c
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Jianhang Xie;Yude Bu;Junchao Liang;Haining Li;Xilu Wang;Jingchang Pan
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作者：
Jianhang Xie;Yude Bu;Junchao Liang;Haining Li;Xilu Wang;Jingchang Pan
极贫金属星（VMP）的[Fe/H] < −2.0 dex。它们是宇宙中最古老的恒星之一，其独特的金属丰度可以帮助探索早期宇宙中恒星化学元素的富集机制和演化历史。然而，目前大多数恒星参数估计方法在确定VMP星的恒星参数方面表现不佳，这限制了我们发现和利用VMP星性质的能力。在这项研究中，我们提出了一个新的模型，基于卷积神经网络来确定恒星大气参数的VMP星。我们在LAMOST光谱上测试了我们的模型;我们的模型可以确定LAMOST光谱的有效温度（T eff）、表面重力（log g）、金属度（[Fe/H]）和碳丰度（[C/Fe]），精度为σ（T eff）= 134.82 K，σ（logg）= 0.33 dex，σ（[Fe/H]）= 0.20 dex，σ（[C/Fe]）= 0.35 dex。此外，我们的模型可以区分VMP星与正常星的准确率为88.65%。我们还将该模型与其他广泛使用的方法进行了比较，发现该方法的性能优于其他方法。它可以应用于即将到来的大型巡天，如4 MOST，WEAVES和MOONS的恒星参数管道，以搜索VMP星和识别碳增强贫金属星。
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 (T eff), surface gravity (log g), metallicity ([Fe/H]), and carbon abundance ([C/Fe]) of the LAMOST spectra with precisions σ(T eff) = 134.82 K, σ(logg) = 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.