基于深度学习的高维光谱分类识别研究

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英文篇名：On classification and recognition of high dimensional stellar spectra based on deep learning 作者：许婷婷 ; 张静敏 ; 杜利婷 ; 周卫红 英文作者：XU Ting-ting;ZHANG Jing-min;DU Li-ting;ZHOU Wei-hong;School of Mathematics and Computer Science, Yunnan Minzu University;Key Laboratory of the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences; 关键词：高维光谱 ; 特征学习 ; 分类识别 ; 深度学习 ; 深度信念网络 英文关键词：high dimensional spectra;;feature learning;;classification and recognition;;deep learning;;deep belief network 中文刊名：云南民族大学学报(自然科学版) 英文刊名：Journal of Yunnan Minzu University(Natural Sciences Edition) 机构：云南民族大学数学与计算机科学学院;中国科学院天体结构与演化重点实验室; 出版日期：2019-05-17 15:39 出版单位：云南民族大学学报(自然科学版) 年：2019 期：03 基金：国家自然科学基金(61561053);; 云南民族大学研究生创新基金(2018YJCXS222) 语种：中文; 页：79-82 页数：4 CN：53-1192/N ISSN：1672-8513 分类号：TP183;P237

摘要

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.

引文

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