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(sixth data,DR6)已对全球的天文工作者开放.恒星光谱数据分类在研究天文观测数据分析领域中极为重要,为了同时兼顾快速的运行速度和准确的分类精度,基于偏差估计卷积神经网络方法(bias estimation convolu-tional neural network,BECNN),分析了DR5中F、G、K、M型恒星光谱.BECNN核心思想主要是利用偏差函数泰勒展开式的偏差参数代替柔性最大值传输函数的偏差参数,进而减小误差,提高准确度.将本文方法与现有的神经网络(neural network,NN)和卷积神经网络(convolutional neural network,CNN)算法进行对比,BECNN算法在F、G、K、M型恒星光谱自动分类准确率分别为93.177％、88.349％、93.807％、89.255％;CNN算法分别为91.646％、87.671％、92.701％、89.054％;NN算法分别为90.819％、87.417％、91.325％、88.092％.同时,将两两恒星光谱数据融合作为测试样本集,做进一步验证.结果表明:BECNN光谱自动分类准确率高于CNN和NN方法,在今后特殊天体索搜与恒星光谱精细分类研究中,本文方法有较好的借鉴价值.

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.