arXiv 2007.115982020-07astro-ph.SR

白矮星光谱分析的计算工具

Computational Tools for the Spectroscopic Analysis of White Dwarfs

Vedant Chandra, Hsiang-Chih Hwang, Nadia L. Zakamska, Tamás Budavári

备注：: 11 pages, 7 figures. Accepted for publication in MNRAS; updated references
DOI：: 10.1093/mnras/staa2165

摘要：

白矮星的光谱特征是在其恒星光球层的较薄上层形成的。这些特征携带着关于白矮星表面温度、表面重力和化学成分的信息（以下简称“标签”）。现有的方法来确定这些标签依赖于复杂的从头算理论模型，并不总是公开。在这里，我们提出了两种技术来确定大气标签从白矮星光谱：生成拟合管道，内插理论光谱与人工神经网络，和一个随机森林回归模型，使用来自吸收线功能的参数。我们使用斯隆数字巡天（SDSS）的一个大型白矮星目录来测试和比较我们的方法，与以前的研究相比，达到了相同的精度和可忽略的偏差。我们将我们的技术打包到一个开源的Python模块“wdtools”中，该模块提供了一种计算成本低廉的方法来从任何设施观察到的白矮星光谱中确定恒星标签。随着更多的理论模型公开，我们将积极开发和更新我们的工具。我们讨论了我们的工具在其目前的形式，以确定有趣的离群白矮星系统，包括那些与磁场，富氦大气，和双退化双星的应用。

英文摘要：

The spectroscopic features of white dwarfs are formed in the thin upper layer of their stellar photosphere. These features carry information about the white dwarf's surface temperature, surface gravity, and chemical composition (hereafter 'labels'). Existing methods to determine these labels rely on complex ab-initio theoretical models which are not always publicly available. Here we present two techniques to determine atmospheric labels from white dwarf spectra: a generative fitting pipeline that interpolates theoretical spectra with artificial neural networks, and a random forest regression model using parameters derived from absorption line features. We test and compare our methods using a large catalog of white dwarfs from the Sloan Digital Sky Survey (SDSS), achieving the same accuracy and negligible bias compared to previous studies. We package our techniques into an open-source Python module 'wdtools' that provides a computationally inexpensive way to determine stellar labels from white dwarf spectra observed from any facility. We will actively develop and update our tool as more theoretical models become publicly available. We discuss applications of our tool in its present form to identify interesting outlier white dwarf systems including those with magnetic fields, helium-rich atmospheres, and double-degenerate binaries.

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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