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arXiv 2207.09415astro-ph.SRastro-ph.IM
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LOTUS：一个（非）LTE的恒星大气参数统一推导优化工具

LOTUS: A (non-)LTE Optimization Tool for Uniform derivation of Stellar atmospheric parameters

Yangyang Li, Rana Ezzeddine

备注：
42 pages, 12 figures, 3 tables, accepted for publication in AJ. For Code on Github, see: https://github.com/Li-Yangyang/LOTUS. For Code Documentation, see: https://lotus-nlte.readthedocs.io/en/latest/
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摘要：

精确的大气恒星基本参数和恒星中单个元素丰度的测定对于所有恒星星族研究都是重要的。非局部热力学平衡（Non-LTE;下文称为NLTE）模型对于这种高精度通常是重要的，然而，可能在计算上复杂且昂贵，这使得模型在光谱分析中较少使用。为了减轻这些模型的计算负担，我们开发了一个强大的一维，LTE和NLTE的基本大气恒星参数推导工具，$\texttt{LOTUS}$，确定有效温度$T_{\mathrm{eff}}$，表面重力$\log g$，金属丰度$\mbox{[Fe/H]}$和微湍流速度$v_{\mathrm{mic}}$ FGK型恒星，从Fe I和Fe II线的等效宽度（EW）测量。我们利用一个广义的生长曲线的方法，考虑到EW的依赖关系，每个Fe I和Fe II线上相应的大气恒星参数。然后，使用全局差分进化优化算法来获得优化的基本参数。此外，$\texttt{LOTUS}$可以使用马尔可夫链蒙特卡罗（MCMC）算法确定每个恒星参数的精确不确定性。我们测试和应用$\texttt{LOTUS}$的基准星的样本，以及从K2的调查，并从$R$ -过程联盟（RPA）调查的贫金属星与可用的星震表面重力。我们发现非常好的协议之间的NLTE派生参数$\texttt{LOTUS}$非光谱值内$T_{\mathrm{eff}}=\pm 30$ K和$\log g=\pm 0.10$ dex的基准星。我们提供开放访问我们的代码，以及Github上提供的插值预计算NLTE EW网格，以及Readthedocs上的工作示例文档。

英文摘要：

Precise fundamental atmospheric stellar parameters and abundance determination of individual elements in stars are important for all stellar population studies. Non-Local Thermodynamic Equilibrium (Non-LTE; hereafter NLTE) models are often important for such high precision, however, can be computationally complex and expensive, which renders the models less utilized in spectroscopic analyses. To alleviate the computational burden of such models, we developed a robust 1D, LTE and NLTE fundamental atmospheric stellar parameter derivation tool, $\texttt{LOTUS}$, to determine the effective temperature $T_{\mathrm{eff}}$, surface gravity $\log g$, metallicity $\mbox{[Fe/H]}$ and microturbulent velocity $v_{\mathrm{mic}}$ for FGK type stars, from equivalent width (EW) measurements of Fe I and Fe II lines. We utilize a generalized curve of growth method to take into account the EW dependencies of each Fe I and Fe II line on the corresponding atmospheric stellar parameters. A global differential evolution optimization algorithm is then used to derive the optimized fundamental parameters. Additionally, $\texttt{LOTUS}$ can determine precise uncertainties for each stellar parameter using a Markov Chain Monte Carlo (MCMC) algorithm. We test and apply $\texttt{LOTUS}$ on a sample of benchmark stars, as well as stars with available asteroseismic surface gravities from the K2 survey, and metal-poor stars from $R$-process Alliance (RPA) survey. We find very good agreement between our NLTE-derived parameters in $\texttt{LOTUS}$ to non-spectroscopic values within $T_{\mathrm{eff}}=\pm 30$ K and $\log g=\pm 0.10$ dex for benchmark stars. We provide open access of our code, as well as of the interpolated pre-computed NLTE EW grids available on Github, and documentation with working examples on Readthedocs.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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