Theoretical stellar spectra rely on model stellar atmospheres computed based on our understanding of the physical laws at play in the stellar interiors. These models, coupled with atomic and molecular line databases, are used to generate theoretical stellar spectral libraries (SSLs) comprising of stellar spectra over a regular grid of atmospheric parameters (temperature, surface gravity, abundances) at any desired resolution. Another class of SSLs is referred to as empirical spectral libraries; these contain observed spectra at limited resolution. SSLs play an essential role in deriving the properties of stars and stellar populations. Both theoretical and empirical libraries suffer from limited coverage over the parameter space. This limitation is overcome to some extent by generating spectra for specific sets of atmospheric parameters by interpolating within the grid of available parameter space. In this work, we present a method for spectral interpolation in the optical region using machine learning algorithms that are generic, easily adaptable for any SSL without much change in the model parameters, and computationally inexpensive. We use two machine learning techniques, Random Forest (RF) and Artificial Neural Networks (ANN), and train the models on the MILES library. We apply the trained models to spectra from the CFLIB for testing and show that the performance of the two models is comparable. We show that both the models achieve better accuracy than the existing methods of polynomial based interpolation and the Gaussian radial basis function (RBF) interpolation.
Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping
Electrical and Computer Engineering, Northwestern University（西北大学电气与计算机工程系）；Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University（跨学科天体物理研究中心（CIERA），西北大学）；NSF-Simons AI Institute for the Sky (SkAI)（国家科学基金会-模拟人工智能天文研究所（SkAI））；Department of Physics and Astronomy, Northwestern University（西北大学物理与天文学系）；Department of Physics, University of Florida（佛罗里达大学物理系）；Institute for Fundamental Theory（基础理论研究所）；Gravitational Wave Science Center (GWSC), Université de Genève（引力波科学中心（GWSC），日内瓦大学）
The Payne: self-consistent ab initio fitting of stellar spectra
我们提出的佩恩，一个通用的方法，精确和同时测定众多的恒星标签从观测光谱，拟合物理光谱模型的基础上。Payne结合了许多重要的方法学方面：它利用了来自大部分可用光谱范围的信息;它同时适合所有标签（恒星参数和元素丰度）;它使用光谱模型，其中大气结构和辐射传输被一致地计算以反映恒星标签。The Payne的核心是在高维标签空间中准确和精确地插值和预测光谱的方法，该方法灵活而强大，但仅基于中等数量的从头算模型（O（1000），25个标签）。通过一个简单的神经网络函数形式和适当的训练标签选择，这种插值在$T_{\rm eff}$和log g（包括矮星和巨星）的宽范围内产生了优于$10^{-3}$ rms的光谱通量预测。我们通过将其应用于APOGEE DR 14数据集来说明这种方法的强大功能，并利用最近改进的线列表绘制Kurucz模型：在没有重新校准的情况下，我们获得了物理上敏感的恒星参数以及15个元素丰度，这些元素丰度似乎比公布的APOGEE DR 14值更精确。简而言之，佩恩是第一次结合所有这些关键成分的方法，这些成分对于测量光谱的最佳建模是必要的;它导致了基于物理模型和无需重新校准的恒星标签的精确和准确的估计。代码和目录都在网上公开提供。
Stellar Spectral Signatures in High-Redshift Galaxies
恒星的发射线和吸收线通常在星系中用8- 10米级望远镜上的摄谱仪在红移高达5处观测到。虽然整体光谱是很好的理解，并已成功地使用经验和理论库建模，一些挑战仍然存在。讨论了三个问题：利用恒星和星际谱线确定丰度，了解强恒星He II 1640谱线的起源，并测量恒星Lyman-α对恒星+星云组合剖面的影响。所有这三个问题都可以通过最近建立的热恒星理论恒星库来解决，这些恒星库考虑到了恒星风的辐射流体动力学。
