Abstract
In the past decade, automated astronomical observatories collected huge amounts of data which can no longer be explored by astronomers individually. In our case, we deal with optical spectra produced by multiobject low-resolution spectrographs.
Due to lower resolution and higher level of noise in such surveys, individual spectra rarely offer reliable information; however, since many similar objects expectedly exist in the universe, 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.