Evolutionary synthesis of automatic classification on astroinformatic big data

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

Keywords:

• Be stars
• stellar spectra
• classification
• evolutionary synthesis
• analytic programming
• SOMA
• big data

Acknowledgements

Special thanks belongs also to the Astronomical Institute of the Academy of Sciences of the Czech Republic for datasets based on spectra obtained by Perek 2m Telescope of Ondřejov observatory, Czech Republic.

Notes

No potential conflict of interest was reported by the authors.

1 https://www.it4i.cz/?lang=en.

Additional information

Funding

This work was supported by Grant Agency of the Czech Republic – GACR GACR [P103/13/08195S]; SGS [grant number SGS 2016/175], VSB-Technical University of Ostrava and by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science – LQ1602”.