An Automatic Detection and Classification Method of the Splicing Abnormality in the Stellar Spectra for LAMOST
MENG Fan-long1, PAN Jing-chang1*, YU Jing-jing1, WEI Peng2
1. School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China
2. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
Abstract：Splicing abnormality is a phenomenon of poor continuity spectrum showed in the splicing wavelengths of the red and blue end. In the spectral processing, this problem can be caused by several factors, such as stability of instrument, observation condition, the response function and so on. It has important effect on the spectra quality whether the splicing is normal or not. In the research of this paper we define a tag on the Lamost spectra automatically to evaluate the quality of spectra splicing and it can provide users with a choice when using data. In this paper, a method of automatic detection of splicing abnormality spectra for LAMOST is proposed to improve the work efficiency greatly.With this method, first of all, we get the red end and blue end of the test spectrum in the splicing wavelengths after flux normalized and the feature lines deleted. Then, we fit the continuum in the red and blue end separately. Thirdly, we calculate the differences of flux between the two fitted curves at a series of independent variables with regular intervals. We get the average and standard deviation of the differences and the area of the two curves formed. Based on the statistics above, an evaluation function is presented in this paper which can be used to judge whether the test spectra are normal or not and determine their abnormal class. The method has been proved to have a good effect in the reorganization of splicing abnormality spectra through a mass of experiments.
孟凡龙，潘景昌，于敬敬，韦 鹏. 巡天光谱中拼接异常光谱的自动检测和异常分级方法[J]. 光谱学与光谱分析, 2017, 37(07): 2250-2253.
MENG Fan-long, PAN Jing-chang, YU Jing-jing, WEI Peng. An Automatic Detection and Classification Method of the Splicing Abnormality in the Stellar Spectra for LAMOST. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 2250-2253.
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