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Machine learning in astronomy

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Abstract

Artificial intelligence techniques like machine learning and deep learning are being increasingly used in astronomy to address the vast quantities of data, which are now widely available. We briefly introduce some of these techniques and then describe their use through the examples of star-galaxy classification and the classification of low-mass X-ray binaries into binaries, which host a neutron star and those which host a black hole. This paper is based on a talk given by one of the authors and reviews previously published work and some new results.

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Notes

  1. https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/w3browse.pl.

  2. https://scikit-learn.org/stable/.

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Acknowledgements

This paper is based on a talk given by one of the authors, Ajit Kembhavi at the ‘Astrophysical jets and observational facilities: National perspective’ meeting at ARIES, Nainital in April 2021, which described ML and DL techniques as well as work on star-galaxy classification by Chaini et al. (2022) and on the classification of LMXB by Pattnaik et al. (2021). The authors wish to thank an anonymous referee for suggestions which helped to significantly improve the manuscript. The data underlying this paper are publicly available in the High Energy Astrophysics Science Archive Research Center (HEASARC) at https://heasarc.gsfc.nasa.gov/db-perl/W3Browse/w3browse.pl and the SDSS archives.

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Authors and Affiliations

  1. Inter-University Centre for Astronomy and Astrophysics (IUCAA), Pune, 411007, India

    Ajit Kembhavi

  2. School of Physics and Astronomy, Rochester Institute of Technology, Rochester, NY, 14623, USA

    Rohan Pattnaik

Authors
  1. Ajit Kembhavi
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  2. Rohan Pattnaik
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Corresponding author

Correspondence to Ajit Kembhavi.

Additional information

This article is part of the Special Issue on “Astrophysical Jets and Observational Facilities: A National Perspective”.

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Kembhavi, A., Pattnaik, R. Machine learning in astronomy. J Astrophys Astron 43, 76 (2022). https://doi.org/10.1007/s12036-022-09871-2

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