基于聚类的星系光谱分析  

Spectral Classification of Galaxies Based on Clustering Analysis

作　　者：张茜 张健楠[1,3] 赵永恒[1,2,3] Zhang Xi;Zhang Jiannan;Zhao Yongheng(National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[1]中国科学院国家天文台,北京100101 [2]中国科学院大学,北京100049 [3]中国科学院光学天文重点实验室(国家天文台),北京100101

出　　处：《天文研究与技术》2020年第2期233-243,共11页Astronomical Research & Technology

基　　金：国家自然科学基金(11403059);国家自然科学天文联合基金(U1531242)资助.

摘　　要：各类大型巡天项目产生了海量的天文数据,因此,需要研究适用于大规模数据的光谱自动处理方法.传统的基于谱线检测或BPT(Baldwin,Phillips,Terjevich)诊断图的星系光谱分类方法难以直接应用于星系光谱自动分类,相比之下,基于机器学习的光谱自动分析更适用于海量天文数据的分类研究.提出一种基于双层聚类的星系光谱分析方法.第1层采用k均值聚类算法将星系光谱分为吸收线星系和发射线星系,第2层使用CLARA(Clustering LARge Applications)聚类算法将发射线星系聚为5簇.对LAMOST DR5的星系数据进行实验,结果表明:(1)第1层k均值聚类能够成功将星系光谱分为吸收线星系和发射线星系,聚类簇与基于谱线检测的分类结果基本一致.(2)第2层CLARA聚类结果能够在BPT图中反映出不同的星系类型.(3)光谱聚类结果与颜色星等图分类存在预期的相关性.(4)k均值聚类和CLARA聚类能够适用于大规模数据自动分析处理,聚类结果能够很好地反映星系的物理性质和演化过程,簇心数据可以为光谱自动分类系统提供模板.Various large-scale sky survey plans release massive astronomical data.It is necessary to study the spectral automatic processing methods for large-scale data.It is difficult to apply the traditional galaxy spectral classification methods based on spectral line measurement or BPT diagram to automatic galaxy spectra classification pipeline directly.In contrast,machine learning method is more suitable for the classification and analysis of massive astronomical data.This paper proposes a galaxy spectral analysis method based on double hierarchical clustering.The first layer uses K-means clustering method to classify galaxy spectra into absorption line galaxies and emission line galaxies;the second layer uses Clustering Large Applications clustering algorithm to gather emission line galaxies into five subtypes.We experiment with galaxy spectral data from LAMOST DR5 and analyze the result in detail by spectral line detection,BPT diagram and color magnitude map.The experimental results show that:(1)The first layer K-means clustering can classify Galaxy spectra into absorption line galaxies and emission line galaxies successful,which are consistent with the classification results based on spectral line detection.(2)The results of CLARA cluster in the second layer can reflect different galaxy types in BPT diagram.(3)There is an expected correlation between spectral clustering results and color magnitude classification.(4)The two-layers clustering can be applied to large-scale data automatic analysis and processing.The clustering results can reflect the physical properties and evolution process of the galaxies.And the cluster centers can provide templates for automatic spectral classification pipeline.

关 键 词：LAMOST 聚类 星系光谱分类 大样本光谱分析 

分 类 号：P157.1[天文地球—天文学]