标题： 利用LAMOST低分辨率光谱估算恒星参数 显示英文标题

标题： Estimating Stellar Parameters from LAMOST Low-resolution Spectra

摘要： 大天区面积多目标光纤光谱望远镜（LAMOST）已经获取了数千万颗恒星的低分辨率光谱。 本文研究了这些光谱的参数估计问题。 为此，我们提出了一个名为StarGRU网络（StarGRUNet）的深度学习模型。 该网络进一步应用于从LAMOST低分辨率光谱中估算恒星大气物理参数和 13种元素丰度。 对于信噪比大于或等于$5$的光谱，估计精度分别为$94$千帕和$0.16$幂数，分别针对$T_\texttt{eff}$和$\log \ g$， 在 [C/H]、[Mg/H]、[Al/H]、[Si/H]、[Ca/H]、[Ni/H] 和 [Fe/H] 上的精度为$0.07$至$0.10$幂数， 在 [O/H]、[S/H]、[K/H]、[Ti/H] 和 [Mn/H] 上的精度为$0.10$至$0.16$幂数，而在 [N/H] 和 [Cr/H] 上的精度分别为$0.18$和$0.22$幂数。 该模型相对于现有的模型显示出优势，并且与高分辨率调查具有高度一致性。 我们分别发布了从LAMOST DR8中大约821万条低分辨率光谱计算得出的估计星表、代码、训练好的模型以及实验数据，用于天文学科学研究和数据处理算法研究。 显示英文摘要

摘要： The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigated the parameter estimation problem for these spectra. To this end, we proposed a deep learning model StarGRU network (StarGRUNet). This network was further applied to estimate the stellar atmospheric physical parameters and 13 elemental abundances from LAMOST low-resolution spectra. On the spectra with signal-to-noise ratios greater than or equal to $5$, the estimation precisions are $94$ K and $0.16$ dex on $T_\texttt{eff}$ and $\log \ g$ respectively, $0.07$ dex to $0.10$ dex on [C/H], [Mg/H], [Al/H], [Si/H], [Ca/H], [Ni/H] and [Fe/H], and $0.10$ dex to $0.16$ dex on [O/H], [S/H], [K/H], [Ti/H] and [Mn/H], and $0.18$ dex and $0.22$ dex on [N/H] and [Cr/H] respectively. The model shows advantages over available models and high consistency with high-resolution surveys. We released the estimated catalog computed from about 8.21 million low-resolution spectra in LAMOST DR8, code, trained model, and experimental data for astronomical science exploration and data processing algorithm research respectively.