Classification of Spectra of Emission Line Stars Using Machine Learning Techniques  

作　　者：Pavla Bromová Petr koda Jaroslav Vázn 

机构地区：[1]Faculty of Information Technology, Brno University of Technology [2]Astronomical Institute of the Academy of Sciences of the Czech Republic

出　　处：《International Journal of Automation and computing》2014年第3期265-273,共9页国际自动化与计算杂志（英文版）

基　　金：supported by Czech Science Foundation(No.GACR13-08195S);the project Central Register of Research Intentions CEZMSM0021630528 Security-oriented Research in Information Technology,the specific research(No.FIT-S-11-2);the project RVO:67985815;the Technological agency of the Czech Republic(TACR)project V3C(No.TE01020415);Grant Agency of the Czech Republic-GACR P103/13/08195S

摘　　要：Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.

关 键 词：Be star stellar spectrum feature extraction dimension reduction discrete wavelet transform CLASSIFICATION support vector machines(SVM) clustering. 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]