论文标题
低分辨率光谱I的恒星参数和元素丰度：Lamost DR8的120万巨人
The stellar parameters and elemental abundances from low-resolution spectra I: 1.2 million giants from LAMOST DR8
论文作者
论文摘要
作为一种典型的数据驱动方法，深度学习成为分析天文数据的自然选择。在这项研究中，我们建立了一个深度卷积神经网络，以估算基本的恒星参数$ t \ rm {_ {fef}} $，log G，金属性（[m/h]和[fe/h]）和[$α$/m]）和[$α$/m] [si/fe]，[Ca/fe]，[mn/fe]，[ni/fe]）。神经网络是在升天调查和Lamost调查之间使用普通恒星训练的。我们将Lamost调查中的低分辨率光谱作为输入，并从Apogee用作标签。对于在测试集中大于10的恒星光谱具有大于10的信噪比，平均绝对误差（MAE）为29 k，对于$ t \ rm {_ {_ {eff}} $，0.07 dex for G，0.03 dex for g，0.03 dex for [fe/h]和[m/h]和[m/h]和0.02 dex for [m/h]和0.02 dex for [$ n $/$ $/m] [$/h]。大多数元素的MAE在0.02 DEX和0.04 DEX之间。训练有素的神经网络适用于1,210,145个巨人，包括子巨头，来自Lamost DR8的恒星参数范围3500 K <$ t \ rm {_ {eff}} $ <5500 k，0.0 dex dex <log G <4.0 dex，-2.5 dex，-2.5 dex dex dex dex dex <[fe/h] <0.5 dex。我们在化学空间中的结果的分布与Apogee标签高度一致，而恒星参数显示出与Galah的外部高分辨率测量结果一致。这项研究的结果使我们能够基于LAMOST数据进一步研究，并加深我们对银河系的积聚和进化历史的理解。该增值目录的电子版本可在http://www.lamost.org/dr8/v1.1/doc/vac上获得。
As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data nowadays. In this study, we built a deep convolutional neural network to estimate basic stellar parameters $T\rm{_{eff}}$, log g, metallicity ([M/H] and [Fe/H]) and [$α$/M] along with nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], [Ni/Fe]). The neural network is trained using common stars between the APOGEE survey and the LAMOST survey. We used low-resolution spectra from LAMOST survey as input, and measurements from APOGEE as labels. For stellar spectra with the signal-to-noise ratio in g band larger than 10 in the test set, the mean absolute error (MAE) is 29 K for $T\rm{_{eff}}$, 0.07 dex for log g, 0.03 dex for both [Fe/H] and [M/H], and 0.02 dex for [$α$/M]. The MAE of most elements is between 0.02 dex and 0.04 dex. The trained neural network was applied to 1,210,145 giants, including sub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K < $T\rm{_{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, -2.5 dex < [Fe/H] < 0.5 dex. The distribution of our results in the chemical spaces is highly consistent with APOGEE labels and stellar parameters show consistency with external high-resolution measurements from GALAH. The results in this study allow us to further studies based on LAMOST data and deepen our understanding of the accretion and evolution history of the Milky Way. The electronic version of the value added catalog is available at http://www.lamost.org/dr8/v1.1/doc/vac.
