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Application of Machine Learning Methods for Cross-Matching Astronomical Catalogues

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1620))

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Abstract

The paper presents an approach for the application of machine learning methods for cross-matching astronomical catalogues. Related works on the cross-matching are analyzed and machine learning methods applied are briefly discussed. The approach is applied for cross-matching of three catalogues: Gaia, SDSS and ALLWISE. Experimental results of application of several machine learning methods for cross-matching these catalogues are presented. Recommendations for the application of the approach in astronomical information systems are proposed.

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Notes

  1. 1.

    Software implementation of the algorithms was taken from sklearn [18], lightgbm, and xgboost Python libraries.

  2. 2.

    https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_sdssdr9_best_neighbour.html.

  3. 3.

    https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_allwise_best_neighbour.html.

References

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Acknowledgements

This work uses data from the European Space Agency (ESA) Gaia mission (https://www.cosmos.esa.int/gaia) processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for DPAC was provided by national institutions, in particular institutions participating in the Gaia Multilateral Agreement.

The work is financially supported by the Russian Foundation for Basic Research, project 19-07-01198.

Author information

Authors and Affiliations

  1. Lomonosov Moscow State University, Moscow, Russia

    Aleksandra Kulishova

  2. Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Moscow, Russia

    Dmitry Briukhov

Authors
  1. Aleksandra Kulishova
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  2. Dmitry Briukhov
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Corresponding author

Correspondence to Aleksandra Kulishova .

Editor information

Editors and Affiliations

  1. Space Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Alexei Pozanenko

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

  3. Christian-Albrecht University of Kiel, Kiel, Germany

    Bernhard Thalheim

  4. Universidad Carlos III de Madrid, Getafe, Spain

    Eva Mendez

  5. A. A. Baikov Institute of Metallurgy and Materials Science of RAS (IMET RAS), Moscow, Russia

    Nadezhda Kiselyova

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Kulishova, A., Briukhov, D. (2022). Application of Machine Learning Methods for Cross-Matching Astronomical Catalogues. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-12285-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12284-2

  • Online ISBN: 978-3-031-12285-9

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