论文标题
M矮人的温度和金属性
Temperatures and Metallicities of M dwarfs in the APOGEE Survey
论文作者
论文摘要
M矮人通过其特性和组成对我们对银河系和外球星尺度上的结构和形成的理解具有巨大的潜力。但是，当前的大气模型在最凉的温度下（$ t _ {\ rm eff} <4200 \，$ k）在恒星中重现光谱特征的能力有限，并充分利用了当前和即将进行的大型大规模光谱调查的信息内容。在这里，我们提出了光谱温度，金属性和光谱类型的目录，用于在Apache Point观测值观测点银河系进化实验（Apogee）和Gaia dr2调查中使用Cannon：一种灵活的，数据驱动的频谱模块和参数框架; eff} $，log $ g $，[fe/h]和详细的丰度）高精度。 Using a training sample of 87 M dwarfs with optically derived labels spanning $2860 < T_{\rm eff} < 4130\,$K calibrated with bolometric temperatures, and $-0.5 < $[Fe/H]$ < 0.5\,$dex calibrated with FGK binary metallicities, we train a two-parameter model with predictive accuracy (in交叉验证）至$ 77 \，$ k和$ 0.09 \，$ dex。我们还使用51 M矮人训练一维光谱分类模型，该模型具有Sloan Digital Sky Survey Optical Spectral类型，范围从M0到M6，再到0.7类型的预测精度。我们发现，与1702个源的子样本相比，炮的温度与$ K的$ K $ k的价格相比，与1702个来源相比，与15 fgk+m+m+m binaries的子样本相比，具有颜色衍生的温度为1702个来源。最后，我们在Cannon和Apogee管道（ASPCAP DR14）标签之间的比较发现，ASPCAP系统地偏向于报告更高的温度和M矮人的金属较低的金属率。
M dwarfs have enormous potential for our understanding of structure and formation on both Galactic and exoplanetary scales through their properties and compositions. However, current atmosphere models have limited ability to reproduce spectral features in stars at the coolest temperatures ($T_{\rm eff} < 4200\,$K) and to fully exploit the information content of current and upcoming large-scale spectroscopic surveys. Here we present a catalog of spectroscopic temperatures, metallicities, and spectral types for 5875 M dwarfs in the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Gaia DR2 surveys using The Cannon: a flexible, data-driven spectral-modeling and parameter-inference framework demonstrated to estimate stellar-parameter labels ($T_{\rm eff}$, log$g$, [Fe/H], and detailed abundances) to high precision. Using a training sample of 87 M dwarfs with optically derived labels spanning $2860 < T_{\rm eff} < 4130\,$K calibrated with bolometric temperatures, and $-0.5 < $[Fe/H]$ < 0.5\,$dex calibrated with FGK binary metallicities, we train a two-parameter model with predictive accuracy (in cross-validation) to $77\,$K and $0.09\,$dex respectively. We also train a one-dimensional spectral classification model using 51 M dwarfs with Sloan Digital Sky Survey optical spectral types ranging from M0 to M6, to predictive accuracy of 0.7 types. We find Cannon temperatures to be in agreement to within $60\,$K compared to a subsample of 1702 sources with color-derived temperatures, and Cannon metallicities to be in agreement to within $0.08\,$dex metallicity compared to a subsample of 15 FGK+M or M+M binaries. Finally, our comparison between Cannon and APOGEE pipeline (ASPCAP DR14) labels finds that ASPCAP is systematically biased toward reporting higher temperatures and lower metallicities for M dwarfs.
