基于最近邻方法的类星体与正常星系光谱分类  

Galaxy/Quasar Classification Based on Nearest Neighbor Method

作　　者：李乡儒[1] 卢瑜[1] 周建明[2] 王永俊[1] 

机构地区：[1]华南师范大学数学科学学院,广东广州510631 [2]潍坊教育学院会计与统计学院,山东青州262500

出　　处：《光谱学与光谱分析》2011年第9期2582-2585,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61075033;60805028)资助

摘　　要：随着高质量CCD传感器技术的日渐成熟与广泛应用,以及许多大型巡天计划的相继实施,天体数据量极大,因此天体观测数据的自动识别、分析问题首当其冲。文章在原始测量空间使用最近邻方法(NN)研究了正常星系与类星体光谱的识别问题。正常星系和类星体属于河外天体,一般距离地球较远,其观测光谱会受到许多干扰,所以这两类天体光谱的分类在光谱自动识别研究中具有一定的代表性。同时,采用的NN是模式识别和数据挖掘方面的基准性方法,在许多新方法的评估中,往往以NN方法的性能作为比较对象。从实用价值来说,研究表明,NN方法的类星体和正常星系光谱识别率与文献中复杂方法的最好结果相当,但该文方法不需要进行分类器的训练,利于实时进行增量式学习和并行实现,这对海量光谱数据的快速处理有重要意义。因此,该研究具有重要的理论参考意义和一定的实用价值。With the wide application of high-quality CCD in celestial spectrum imagery and the implementation of many large sky survey programs （e. g. , Sloan Digital Sky Survey （SDSS）, Two-degree-Field Galaxy Redshi{t Survey （2dF）, Spectroscopic Sur- vey Telescope（SST）, Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） program and Large Synoptic Sur- vey Telescope （LSST） program, etc. ）, celestial observational data are coming into the world like torrential rain. Therefore, to utilize them effectively and fully, research on automated processing methods for celestial data is imperative. In the present work, we investigated how to recognizing galaxies and quasars from spectra based on nearest neighbor method. Galaxies and quasars are extragalactic objects, they are far away from earth, and their spectra are usually contaminated by various noise. Therefore, it is a typical problem to recognize these two types of spectra in automatic spectra classification. Furthermore, the utilized method, nearest neighbor, is one of the most typical, classic, mature algorithms in pattern recognition and data mining, and often is used as a benchmark in developing novel algorithm. For applicability in practice, it is shown that the recognition ratio of nearest neighbor method （NN） is comparable to the best results reported in the literature based on more complicated methods, and the superiority of NN is that this method does not need to be trained, which is useful in incremental learning and parallel computation in mass spectral data processing. In conclusion, the results in this work are helpful for studying galaxies and quasars spectra classification.

关 键 词：天体光谱分类 最近邻方法 正常星系 类星体 

分 类 号：TN911.7[电子电信—通信与信息系统]