基于谱线特征匹配的恒星光谱自动识别方法

我国正在实施的大型巡天项目(LAMOST项目)，急需恒星光谱的自动识别系统。文章给出了一种基于谱线特征匹配的恒星光谱自动识别方法。该方法由以下主要步骤组成：(1) 利用小波变换的方法对观测光谱进行谱线特征提取；(2) 将提取出的特征和恒星谱线的特征模板进行相关匹配；(3) 根据相关匹配结果进行恒星光谱识别。通过对Sloan Digital Sky Survey (SDSS)，Data Release Four (DR4)中的大量真实光谱数据实验表明，该方法具有对噪声鲁棒等特点，正确识别率高达96.7%。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，可为天文学家进行恒星和银河系的结构等研究提供帮助。

A Method for Automatic Recognition of Stellar Spectra Based on Feature Matching of Spectral Lines

The LAMOST project,the world's largest sky survey project being implemented in China,urgently needs an automatic stars recognition system.The present paper presents a method for automatic recognition of stellar spectra based on feature matching of spectral lines.This method consists of three main steps: First,the features of spectral lines!in the observed spectra are extracted using the wavelet transform.Then,the correlations between the extracted features and the feature templates of the stellar spectral lines are computed.Finally,based on the results of the former step,the stellar spectra can be recognized.The extensive experiments with real observed spectra from the SDSS DR4 show that the method can robustly recognize stellar spectra,and the correct rate of this method is as high as 96.7%.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 and helps in the structure study of stars and galaxy etc.