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 approache...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.展开更多
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)+2 种基金the project RVO:67985815the Technological agency of the Czech Republic(TACR)project V3C(No.TE01020415)Grant Agency of the Czech Republic-GACR P103/13/08195S
文摘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.
