Spectral classification of stars based on LAMOST spectra

• Published in: Research in astronomy and astrophysics 2015-08, Vol.15 (8), p.1137-1153
• Main Authors: Liu, Chao, Cui, Wen-Yuan, Zhang, Bo, Wan, Jun-Chen, Deng, Li-Cai, Hou, Yong-Hui, Wang, Yue-Fei, Yang, Ming, Zhang, Yong
• Format: Article
• Language: English
• Subjects: Astronomy; Classification; Equivalence; Galaxy: stellar contents; LAMOST; Photometry; Signal to noise ratio; Spectra; Stars; stars: fundamental parameters; stars: general; stars: statistics; Stellar spectra; techniques: spectroscopic; 光谱分类; 光谱特征; 多色测光; 恒星光谱; 支持向量机; 等值宽度; 高信噪比
• ISSN: 1674-4527; 2397-6209
• DOI: 10.1088/1674-4527/15/8/004

In this work, we select spectra of stars with high signal-to-noise ratio from LAMOST data and map their MK classes to the spectral features. The equivalent widths of prominent spectral lines, which play a similar role as multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either broadly used MK classes or stellar astrophysical parameters. We also employ an SVM-based classification algorithm to assign MK classes to LAMOST stellar spectra. We find that the completenesses of the classifications are up to 90% for A and G type stars, but they are down to about 50% for OB and K type stars. About 40% of the OB and K type stars are mis-classified as A and G type stars,respectively. This is likely due to the difference in the spectral features between late B type and early A type stars or between late G and early K type stars being very weak. The relatively poor performance of the automatic MK classification with SVM suggests that the direct use of line indices to classify stars is likely a more preferable choice.