基于光谱特征的恒星自动识别方法  

A Method for Auto-Recognizing the Stars Based on Spectral Feature

作　　者：刘中田[1] 邱宽民[1] 

机构地区：[1]北京交通大学电子信息工程学院,北京100044

出　　处：《光谱学与光谱分析》2010年第1期274-277,共4页Spectroscopy and Spectral Analysis

基　　金：国家高技术研究发展计划("863")项目(2003AA133060);北京交通大学科技基金项目(2006RC028;2007XM004)资助

摘　　要：我国正在实施的大型巡天项目(LAMOST项目),急需恒星光谱自动识别与分类系统并给出了一种基于光谱特征的恒星自动识别方法。该方法由以下主要步骤组成:(1)利用谱线小波特征进行恒星谱线整体估计和恒星Balmer线的检测;(2)利用吸收带小波特征进行吸收带位置和M型星特征频率检测;(3)根据以上检测结果进行发射线星、M型星和早型恒星识别。通过对(sloan digital sky survey,SDSS)(data releasefour,DR4)中的大量真实光谱数据实验表明,方法具有对噪声鲁棒等特点,发射线星识别率达到97.5%,M型星识别率达到98.1%,早型恒星识别率达到96.8%,类星体和星系的误识别率低于2%。该方法可对相对定标的巡天光谱进行自动识别,符合LAMOST数据的要求。The LAMOST project, the world＇s largest sky survey project being implemented in China, urgently needs an automatic stars recognition and classification system. This paper presents a method for auto-recognizing the stars based on spectral feature. This method consists of three main steps.. First, the integral information of spectral lines is calculated and the stellar Balmer lines are detected by using the wavelet features of spectral lines. Then, the characteristic frequency of M-type stars and the locations of absorption bands are obtained accurately through the wavelet features of absorption bands. Finally, based on the results of the former step, the emission-line stars, M-type stars and early-type stars can be recognized. The extensive experi- ments with real observed spectra from the SDSS DR4 show that the method can robustly recognize stellar spectra, the correct rate of the emission-line stars is as high as 97.5%, the correct rate of M-type stars is as high as 98.1 %and the correct rate of early-type stars is as high as 96.8%. The error rate of the quasars and the galaxies is less than 2%. This method is designed to automatically recognize stellar spectra with relative flux and low signal-to-noise ratio, which is applicable to the LAMOST data.

关 键 词：恒星光谱识别 谱线特征匹配 发射线星 M型星 小波变换 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]