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Alternative TitleAutomatic recognition and classification of Stellar Spectra
Thesis Advisor吴福朝 ; 赵永恒
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword天体光谱 自动分类 红移测量 小波变换 离散傅里叶变换 卷积型小波包变换 多尺度特征匹配 Celestial Object Spectra Automated Classification Redshifts Measurement Wavelet Transform Discrete Fourier Transform Convolution Type Of Wavelet Packet Transform Multi-scaling Feature Matching
Abstract我国正在实施的大天区面积多目标光纤光谱天文望远镜项目(简称LAMOST)是国家重大科学工程项目之一,三年观测期内所获得的光谱数据总量将达10000000数量级。本文正是在这种背景下展开的,重点探索了天体光谱数据的预处理、自动分类和红移参数的自动测量方法,以满足LAMOST项目的需求。 本文的工作主要包括以下四个部分: (1) 基于卷积型小波包变换的谱线自动提取方法 该方法首先对观测光谱进行4层卷积型小波包变换,然后对第四层小波包系数,采用区域相关算法以及阈值处理方法进行噪声处理,选择中高频小波包系数进行谱线的小波特征重构,最后根据重构后的小波特征,利用谱线搜索方法,在观测光谱中提取谱线。实验中,我们用恒星、正常星系和活动星系光谱进行谱线提取测试,结果表明所述方法具有对噪声鲁棒和谱线提取准确等特点。 (2) 我们提出了一种自动识别M型星的新方法。该方法选取一定波长范围的光谱进行5层小波变换,从第5层小波系数中提取出小波特征,然后利用小波特征检测M型星特征频率和吸收带位置,根据特征频率和吸收带位置的检测结果进行M型星识别。大量真实光谱数据实验表明,该方法十分有效,识别率高达98.79%。 (3) 恒星和特殊恒星的自动识别 1) 利用基于小波特征的方法构造发射线恒星识别器,将发射线恒星识别出来;2) 利用恒星识别和分类器进行恒星的识别和细分类。本方法通过基于小波特征的方法一定程度上降低了噪声和低分辨率对自动识别的不利影响。 (4) 类星体(QSO)的红移测量方法 为了克服信噪比较低的不利因素,我们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 Two (DR2)中的15,715条类星体光谱的实验表明,在误差为0.02的范围内所述方法的正确率达到95.13%。
Other AbstractThe LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) project is one of the national key scientific projects.This work is particularly focused on finding and designing suitable techniques for spectral pretreatment, classification and redshift measurements. The main contributions are as follows:(1) A method for auto-extraction of spectral lines based on convolution type of wavelet packet transformation.Firstly, the observed spectra are transformed by convolution type of wavelet packet with 4 scales. Then, the noise in the coefficients of the 4th scale is eliminated by local correlation and threshold in the wavelet packet domain. After that, middle and high frequency coefficients are selected to reconstruct the wavelet feature of the spectral lines. Last, based on the reconstructed wavelet feature, spectral lines in observed spectra are searched. The results of our experiments, with the spectral lines of stars, normal galaxies and active galaxies, show that the proposed method can robustly and accurately extract the spectral lines.(2) A novel method for automatic M-type stars recognition is proposed. Firstly, after a wavelet transform with 5 scales on the spectra in a selected wavelength region, the wavelet features are extracted from the transformed coefficients on the 5th scale. Then, the characteristic frequency of M-type stars and the locations of absorption bands are obtained accurately through the wavelet features, and thanks to them, M-type stars in all kinds of celestial bodies are recognized. The extensive experiments with real observed spectra show that the proposed method is effective and its correct recognition rate is as high as 98.79%. (3) The automatic recognition of stars and peculiar stars:1) The emission-line stars (ELS) are recognized by the ELS recognizer by a method based on the wavelet feature. 2) The stars are recognized and classified by the stellar recognizer. The presented method can to some extent alleviate the adverse effect on the recognition by low signal-to-noise ratio and low resolution distance. (4) A method for redshifts measurement of quasars(QSO): Firstly, the features of the broad emission lines are extracted from the quasar spectra to alleviate the difficulties arising from the low signal-to-noise ratio. Then the redshifts of quasar spectra are estimated by using the multi-scaling feature matching. The experiments with the 15,715 quasars from the SDSS DR2 show that the correct estimation rate of redshifts is 95.13% within an error range of 0.02.
Other Identifier200318014603018
Document Type学位论文
Recommended Citation
GB/T 7714
刘中田. 恒星光谱的自动识别与分类方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2006.
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