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Classification of Spectra of Emission Line Stars Using Machine Learning Techniques
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SYSNO ASEP 0430487 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve SCOPUS Title Classification of Spectra of Emission Line Stars Using Machine Learning Techniques Author(s) Bromová, P. (CZ)
Škoda, Petr (ASU-R) RID, ORCID
Vážný, Jaroslav (ASU-R)Source Title International Journal of Automation and Computing - ISSN 1476-8186
Roč. 11, č. 3 (2014), s. 265-273Number of pages 9 s. Publication form Print - P Language eng - English Country CN - China Keywords Be star ; stellar spectrum ; feature extraction Subject RIV BN - Astronomy, Celestial Mechanics, Astrophysics R&D Projects GA13-08195S GA ČR - Czech Science Foundation (CSF) Institutional support ASU-R - RVO:67985815 EID SCOPUS 84902345709 DOI 10.1007/s11633-014-0789-2 Annotation 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. Workplace Astronomical Institute Contact Radka Svašková, bibl@asu.cas.cz, Tel.: 323 620 326 Year of Publishing 2015
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