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基于小波多尺度特征匹配的类星体红移测量方法
引用本文:刘中田,李乡儒,吴福朝,赵永恒.基于小波多尺度特征匹配的类星体红移测量方法[J].光谱学与光谱分析,2006,26(9):1738-1741.
作者姓名:刘中田  李乡儒  吴福朝  赵永恒
作者单位:1. 中国科学院自动化研究所模式识别国家重点实验室, 北京 100080
2. 中国科学院国家天文台, 北京 100012
基金项目:国家高技术研究发展计划(863计划) , LAMOST项目
摘    要:在中国正在实施的大型巡天项目(LAMOST项目)中,预计能获得105数量级的类星体光谱。文章旨在研究适用于LAMOST观测数据的类星体红移测量方法。为了克服信噪比较低的不利因素,文章采用小波变换的方法对类星体宽发射线进行特征提取,然后利用多尺度特征匹配的方法进行类星体红移测量。通过对sloan digital sky survey(SDSS) data release 2(DR2)中的15, 715条类星体光谱的实验表明,在误差为0.02的范围内所用方法的正确率达到95.13%。该方法可对相对定标的类星体光谱数据进行红移测量,符合LAMOST数据的要求,可为天文学家进行类星体和宇宙大尺度等研究提供帮助。

关 键 词:小波变换  特征提取  特征匹配  宽发射线  
文章编号:1000-0593(2006)09-1738-04
收稿时间:2005-06-06
修稿时间:2005-10-20

A Method for Obtaining Redshifts of Quasars Based on Wavelet Multi-Scaling Feature Matching
LIU Zhong-tian,LI Xiang-ru,WU Fu-chao,ZHAO Yong-heng.A Method for Obtaining Redshifts of Quasars Based on Wavelet Multi-Scaling Feature Matching[J].Spectroscopy and Spectral Analysis,2006,26(9):1738-1741.
Authors:LIU Zhong-tian  LI Xiang-ru  WU Fu-chao  ZHAO Yong-heng
Institution:1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China2. National Astronomical Observatory, Chinese Academy of Sciences, Beijing 100012, China
Abstract:The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc.
Keywords:Wavelet transform  Feature extraction  Feature matching  Broad emission line
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