论文标题
来自Lamost DR7的光谱特征的原发性红色恒星的识别，质量和年龄
Identification, mass and age of primary red clump stars from spectral features derived with the LAMOST DR7
论文作者
论文摘要
尽管红色团块（RC）恒星由于其亮度和颜色的稳定性而易于识别，但大约20-50％的恒星实际上是在HR图上同一位置的红色巨型分支（RGB）恒星。在本文中，鉴定出了来自Lamost DR7的184个初级RC（PRC）恒星的210,504光谱样品，其纯度高于90％。通过采用XGBoost集合学习算法，通过Lamost光谱（R〜1800和SNR> 10）成功区分了RC和RGB恒星，并删除了次级RC恒星。 Shapley添加说明（SHAP）值用于解释XGBoost模型选择的顶部功能。这些功能约为FE5270，MGH＆MGIB，FE4957，FE4207，CR5208和CN，它们可以成功区分RGB和RC星。 XGBoost还用于通过用标记为星线震的Kepler训练其光谱来估计PRC恒星的年龄和质量。质量和年龄的不确定性分别为13％和31％。验证特征归因模型，我们发现对年龄敏感的元素XGBoost获得的元素与文献一致。 PRC恒星的距离是由Gaia Edr3校准的$ K_ {S} $绝对幅度得出的，Gaia Edr3的不确定性约为6％，并显示恒星主要位于银河系磁盘。我们还使用R $ \ sim $ 250测试XGBoost，这是中国空间站望远镜（CSST）正在建设中的分辨率，它仍然能够找到敏感的功能来区分RC和RGB。
Although red clump (RC) stars are easy to identify due to their stability of luminosity and color, about 20-50% are actually red giant branch (RGB) stars in the same location on the HR diagram. In this paper, a sample of 210,504 spectra for 184 318 primary RC (PRC) stars from the LAMOST DR7 is identified, which has a purity of higher than 90 percent. The RC and the RGB stars are successfully distinguished through LAMOST spectra(R~1800 and SNR>10) by adopting the XGBoost ensemble learning algorithm, and the secondary RC stars are also removed. The SHapley Additive exPlanations (SHAP) value is used to explain the top features that the XGBoost model selected. The features are around Fe5270, MgH & MgIb, Fe4957, Fe4207, Cr5208, and CN, which can successfully distinguish RGB and RC stars. The XGBoost is also used to estimate the ages and masses of PRC stars by training their spectra with Kepler labeled asteroseismic parameters. The uncertainties of mass and age are 13 and 31 percent, respectively. Verifying the feature attribution model, we find the age-sensitive elements XGBoost gets are consistent with the literature. Distances of the PRC stars are derived by $K_{S}$ absolute magnitude calibrated by Gaia EDR3, which has an uncertainty of about 6 percent and shows the stars mainly locate at the Galactic disk. We also test the XGBoost with R$\sim$250, which is the resolution of the Chinese Space Station Telescope(CSST) under construction, it is still capable of finding sensitive features to distinguish RC and RGB.
