Employing Evolutionary Algorithms for Classification of Astrophysical Spectra.
David Bednárek, Martin Kruliš, Jakub Yaghob, Filip Zavoral · 2014
Abstract: In the past decade, automated astronomical ob-servatories collected huge amounts of data which can no longer be explored by astronomers individually. In our case, we deal with optical spectra produced by multi-object low-resolution spectrographs. Due to lower res-olution and higher level of noise in such surveys, indi-vidual spectra rarely offer reliable information; however, since many similar objects expectedly exist in the uni-verse, global analysis of the spectrum database may reveal classes of objects sharing similar properties. In this paper, we propose a novel evolutionary approach to classification of spectral data which is expected to achieve finer level of detail than traditional methods. Furthermore, we describe the most computationally-intensive parts of the method in the form of parallel cache-aware algorithm.