论文标题
使用光学恒星模板对单星和光谱二进制进行分类
Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates
论文作者
论文摘要
恒星光谱分类是现代天文学的基本工具，可洞悉诸如有效温度，表面重力和金属性等物理特征。准确，快速的频谱键入是对SDSS和Lamost等大型全天花观调查的积分需求。在这里，我们介绍了使用光谱模板和光谱线索引测量值的Pyhammer恒星光谱分类软件。 Pyhammer v2.0扩展了分类功率，包括碳（C）恒星，DA White Dwarf（WD）恒星以及双线光谱二进制（SB2）。该版本还包括一个新的亮度归一化光谱的经验库，可用于通量校准观察到的光谱，或创建合成的SB2光谱。我们已经作为模板生成了物理合理的SB2组合，从而增加了Pyhammer的SB2型能力。我们测试了从SDSS产生的SB2光谱上的分类成功率，这些频谱类型和信噪比。在所描述的配对范围内，正确分类了超过$ 95 \％的SB2。
Stellar spectral classification is a fundamental tool of modern astronomy, providing insight into physical characteristics such as effective temperature, surface gravity, and metallicity. Accurate and fast spectral typing is an integral need for large all-sky spectroscopic surveys like the SDSS and LAMOST. Here, we present the next version of PyHammer, stellar spectral classification software that uses optical spectral templates and spectral line index measurements. PyHammer v2.0 extends the classification power to include carbon (C) stars, DA white dwarf (WD) stars, and also double-lined spectroscopic binaries (SB2). This release also includes a new empirical library of luminosity-normalized spectra that can be used to flux calibrate observed spectra, or to create synthetic SB2 spectra. We have generated physically reasonable SB2 combinations as templates, adding to PyHammer the ability to spectrally type SB2s. We test classification success rates on SB2 spectra, generated from the SDSS, across a wide range of spectral types and signal-to-noise ratios. Within the defined range of pairings described, more than $95\%$ of SB2s are correctly classified.
