Title: Physical Parameters of Stars in NGC 6397 Using ANN-Based Interpolation and Full Spectrum Fitting Show Chinese title

Title: 基于人工神经网络插值和全谱拟合的NGC 6397中恒星的物理参数

Abstract: Stellar spectral interpolation is critical technique employed by fitting software to derive the physical parameters of stars. This approach is necessary because on-the-go generation of synthetic stellar spectra is not possible due to the complex and high cost of computation. The goal of this study is to develop a spectral interpolator for a synthetic spectral library using artificial neural networks (ANNs). The study aims to test the accuracy of the trained interpolator through self-inversion and, subsequently, to utilize the interpolator to derive the physical parameters of stars in the globular cluster NGC 6397 using spectra obtained from the Multi Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT). In this study, ANNs were trained to function as spectral interpolators. The ULySS full-spectrum fitting package, integrated with the trained interpolators, was then used to extract the physical parameters of 1587 spectra of 1063 stars in NGC 6397. The trained ANN interpolator achieved precise determination of stellar parameters with a mean difference of 31 K for $T_{\rm eff}$ and 0.01 dex for [Fe/H] compared to previous studies. This study demonstrates the efficacy of ANN-based spectral interpolation in stellar parameter determination, offering faster and more accurate analysis. Show Chinese abstract

Abstract: 恒星光谱插值是拟合软件用来推导恒星物理参数的关键技术。 这种方法是必要的，因为由于计算的复杂性和高成本，无法实时生成合成恒星光谱。 本研究的目标是使用人工神经网络（ANNs）开发一个合成光谱库的光谱插值器。 本研究旨在通过自反演测试训练好的插值器的准确性，随后利用该插值器从甚大望远镜（VLT）上的多单元光谱探测器（MUSE）获取的光谱中推导球状星团NGC 6397中恒星的物理参数。 在本研究中，ANNs被训练为光谱插值器。 然后将集成训练好的插值器的ULySS全谱拟合包用于提取NGC 6397中1063颗恒星的1587条光谱的物理参数。 与之前的研究相比，训练好的ANN插值器在$T_{\rm eff}$上平均差异为31 K，在[Fe/H]上为0.01 dex，实现了恒星参数的精确确定。 本研究表明基于ANN的光谱插值在恒星参数确定中的有效性，提供了更快更准确的分析。