Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

0430487 - ASÚ 2015 RIV CN eng J - Journal Article
Bromová, P. - Škoda, Petr - Vážný, Jaroslav
Classification of Spectra of Emission Line Stars Using Machine Learning Techniques.
International Journal of Automation and Computing. Roč. 11, č. 3 (2014), s. 265-273. ISSN 1476-8186
R&D Projects: GA ČR GA13-08195S
Institutional support: RVO:67985815
Keywords : Be star * stellar spectrum * feature extraction
Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics

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.
Permanent Link: http://hdl.handle.net/11104/0235450