基于深度学习的高维光谱分类识别研究

On classification and recognition of high dimensional stellar spectra based on deep learning

光谱分类识别一直是天文学家研究中的基础问题,也是LAMOST巡天计划的一项重要任务.从LAMOST发布的海量天体光谱数据库中选取F、G、K 3种型星光谱数据,采用深度学习模型进行分类识别研究和对比实验研究,解决原有方法对光谱分类可信度低的问题.实验结果证明:对于F、G、K 3种型星的分类精确度问题,深度学习方法明显优于原有其他分类方法.

Spectral classification and recognition has always been the basic problem in the astronomical studies, and it is also an important task of LAMOST sky survey project. This paper selects three types (F, G and K) of spectra data from LAMOST, and gives them a comparison of the criteria for classification and recognition based on a deep learning model in order to solve the problem of low credibility of the spectral classification by the traditional classification method. The results show that this deep learning method is superior to other classification methods for the classification accuracy of the above-mentioned types of stars.