Evolutionary synthesis of automatic classification on astroinformatic big data. Issue 5 (3rd September 2017)

• Record Type: Journal Article
• Authors: Kojecký, Lumír; Zelinka, Ivan; Šaloun, Petr
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 32:Issue 5(2017)
• Page Start: 429
• Page End: 447
• Publication Date: 2017-09-03
• Subjects: Be stars -- stellar spectra -- classification -- evolutionary synthesis -- analytic programming -- SOMA -- big data; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2016.1194984
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract: This article describes using of new approach to automatic classification of big data records in Be and B[e] stars spectra in large astrophysical archives. With enormous amount of these data it is no longer feasible to analyse it using classical approaches. We introduce evolutionary synthesis of the classification by means of so called analytic programming (AP), one of methods of symbolic regression. By using this method, we synthesise the most suitable mathematical models that approximate chosen samples of the stellar spectra. As a result is then selected the class whose synthesised formula has the lowest difference (i.e. the most similar) compared to the particular spectrum. The results show us that classification of stellar spectra by means of AP is able to identify different shapes of the spectra and classify them. Graphical Abstract: