利用光学恒星模板分类单星和光谱双星
Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates
摘要：
恒星光谱分类是现代天文学的一个基本工具，它提供了对有效温度、表面重力和金属丰度等物理特征的深入了解。准确和快速的光谱类型是大型全天空光谱调查（如SDSS和LAMOST）的必要条件。在这里，我们提出了下一个版本的PyHammer，恒星光谱分类软件，使用光谱模板和谱线指数测量。PyHammer v2.0扩展了分类能力，包括碳（C）星、DA白矮星（WD）星和双线分光双星（SB 2）。此版本还包括一个新的亮度归一化光谱的经验库，可用于通量校准观测光谱，或创建合成SB2光谱。我们已经生成了物理上合理的SB2组合作为模板，为PyHammer添加了光谱类型SB2的能力。我们测试的SB2光谱，从SDSS生成的分类成功率，在广泛的光谱类型和信噪比。在所述配对的定义范围内，超过$95\%$的SB 2被正确分类。
英文摘要：
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.
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