基于偏差估计卷积神经网络恒星光谱数据自动分类

天体物理学科中恒星光谱具有极其重要的研究前景,中国自主研制的大科学天文巡天项目大天区多目标光纤光谱望远镜(large sky area multi-object fiber spectroscopy telescope,LAMOST)自启用以来,已经成为世界上空间光谱获取数据量最大的科学装置.目前,第6期数据(sixt...

Automatic Classification of Massive Stellar Spectral Data based on Bias Estimation Convolutional Neural Network

The stellar spectroscopy in the Department of Astrophysics has extremely important research prospects. Since the LAMOST (Large Sky Area Multi-Object Fiber Spectroscopy Telescope), a large-scale scientific astronomy survey project independently developed by China which has been the world''s largest scientific device for obtaining space spectroscopy data. The sixth data (DR6) is currently available to astronomers around the world. The classification of stellar spectral data is extremely important in studying the content of astronomical observation data analysis. In order to take into account both fast operating speed and accurate classification accuracy, this paper analyzes F, G, K, M-type star spectrum in DR5 based on the bias estimation convolutional neural network method (BECNN). The core idea of BECNN is to replace the bias parameter of the flexible maximum transfer function with the bias parameter of the Taylor expansion of the deviation function, thereby reducing the error and improving the accuracy. Comparing this method with the existing two algorithms, the Neural Network (NN) and the Convolutional Neural Network (CNN). The results show that the automatic classification accuracy of the BECNN algorithm in F, G, K, and M star spectra is 93.177%, 88.349%, 93.807%, 89.255%; the CNN algorithm is 91.646%, 87.671%, 92.701%, 89.054%; the NN algorithm is 90.819%, 87.417%, 91.325%, 88.092%, and the fusion of pairwise star spectral data is used as a test Sample set for further verification. It is concluded that the automatic classification accuracy of BECNN spectrum is higher than CNN and NN. This method has good reference value in future research on special celestial body search and fine classification of star spectrum.