早M型矮恒星光谱聚类方法与分析  

Research of Clustering for LAMOST Early M Type Spectra

作　　者：刘杰[1] 潘景昌[1] 吴明磊 刘聪[1] 韦鹏[3] 衣振萍[1] 刘猛[1] 

机构地区：[1]山东大学(威海)机电与信息工程学院,山东威海264209 [2]哈尔滨理工大学(荣成校区),山东威海264209 [3]中国科学院光学天文重点实验室,中国科学院国家天文台,北京100012

出　　处：《光谱学与光谱分析》2017年第12期3904-3907,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(U1431102,11603014)资助

摘　　要：大规模光谱巡天项目如LAMOST等产生了海量极具研究价值的观测数据,如何对此数量级的数据进行有效的分析是当前的一个研究热点。聚类算法是一类无监督的机器学习算法,可以在不依赖于领域知识的情况下对数据进行处理,发现其中的规律与结构。恒星光谱聚类是天文数据处理中一项非常重要的工作,主要对海量光谱巡天数据按照其物理及化学性质分类。针对LAMOST巡天中的早M型矮恒星的光谱数据,使用多种聚类算法如K-Means,Bisecting K-Means和OPTICS算法做了聚类分析,研究不同聚类算法在早M型恒星数据的表现。聚类算法在一定程度依赖于其使用的距离度量算法,同时研究了欧氏距离、曼哈顿距离、残差分布距离和上述三种聚类算法搭配下的表现。实验结果表明:(1)聚类算法可以很好地辅助分析早M型矮恒星的光谱数据,聚类产生的簇心数据和MK分类吻合得非常好。(2)三种不同聚类算法表现不尽相同,Bisecting K-Means在恒星光谱细分类方面更有优势。(3)在聚类的同时也会产生一些数量较少的簇,从这些簇中可以发现一些稀有天体候选体,相对而言OPTICS适合用来寻找稀有天体候选体。Large-scale spectral survey projects such as LAMOST produce a great deal of valuable research data ,and how to ef-fectively analyze the data of this magnitude is a current research hotspot .Clustering algorithm is a kind of unsupervised machine learning algorithm ,which makes the clustering algorithm deal with the data without knowledge of the domain ,and internal law and structure will be found out .Stellar spectral clustering is a very important work in astronomical data processing .It mainly classifies the mass spectral survey data according to its physical and chemical properties .In this paper ,we use a variety of clus-tering algorithms such as K-Means ,Bisecting K-Means and OPTICS to do clustering analysis for the early M-type stellar data in LAMOST survey .The performance of these algorithms on the early M-type stellar data is also discussed .In this paper ,the per-formance of the Euclidean distance ,the Manhattan distance ,the residual distribution distance for the three clustering algorithms are studied ,and the clustering algorithm depends on the distance measurement algorithm .The experimental results show that ：（1） The clustering algorithm can well analyze the spectral data of the early M-type dwarf star ,and the cluster data produced by clustering is very good with the MK classification .（2） The performance of the three different clustering algorithms is different , and Bisecting K-Means has more advantages in stellar spectral subdivision .（3） In the cluster at the same time it will produce some small number of clusters ,and some rare celestial bodies can be found from these clusters .OPTICS is relatively suitable for finding rare objects .

关 键 词：LAMOST 聚类 降维 

分 类 号：P145.4[天文地球—天体物理]