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Hierarchical Multiple Stellar Systems

Part of the Communications in Computer and Information Science book series (CCIS,volume 706)

Abstract

In astrophysics of hierarchical multiple stellar systems there is a contradiction between their maximum observed multiplicity (up to seven) and its theoretical limitations (about five hundred). To search for the hierarchical systems of high multiplicity we have analyzed modern catalogues of wide and close stellar pairs. We have compiled a list of objects - candidates for the stellar systems of maximum multiplicity, which includes an accurate cross-identification of their components. Presented procedure of cross-matching of multiple stellar systems is based on applying of criteria of unified form that exclude objects from sets of possible candidates to identification. Criteria are constructed using domain knowledge on astronomical objects of certain type. These criteria are not dependent on source catalogues but take into account knowledge on specific features of objects depending on conditions of observation.

Keywords

  • Binary stars
  • Visual binaries
  • Cross-matching
  • Conceptual modeling
  • Multi-component
  • Entity resolution

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Acknowledgments

The work was partly supported by the Presidium of the Russian Academy of Sciences program “Leading Scientific Schools Support” 9951.2016.2 and by the Russian Foundation for Basic Research grants 15-02-04053, 16-07-01028, and 16-07-01162.

This research has made use of the VizieR catalogue access tool (the original description of the VizieR service was published in A&AS 143, 23), the SIMBAD database, operated at the Centre de Donnees astronomiques de Strasbourg, the Washington Double Star Catalog maintained at the U.S. Naval Observatory, and NASA’s Astrophysics Data System Bibliographic Services.

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Correspondence to Nikolay A. Skvortsov .

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Skvortsov, N.A., Kalinichenko, L.A., Kovaleva, D.A., Malkov, O.Y. (2017). Hierarchical Multiple Stellar Systems. In: Kalinichenko, L., Kuznetsov, S., Manolopoulos, Y. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2016. Communications in Computer and Information Science, vol 706. Springer, Cham. https://doi.org/10.1007/978-3-319-57135-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-57135-5_9

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