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Evaluation of Binary Star Formation Models Using Well-Observed Visual Binaries

Part of the Communications in Computer and Information Science book series (CCIS,volume 1003)

Abstract

Creation of the Galaxy model describing formation and evolution of binary stars requires generating and testing hypotheses related to the process of formation of binary stars and distributions of their characteristics. A set of hypotheses can be generated on the basis of a number of publications that suggested the formation of binary systems. We describe the project aimed at finding initial distributions of binary stars over masses of components, mass ratios of them, semi-major axes and eccentricities of orbit, and also pairing scenarios by means of Monte-Carlo modeling of the sample of visual binaries of luminosity class V with a set of additional restrictions, so it can be considered as free of observational incompleteness effects. We present results which allow rejecting some estimated initial distributions of visual binary star parameters.

Keywords

  • Binary stars
  • Stellar formation
  • Modeling

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Acknowledgments

We are grateful to T. Kouwenhoven, A. Malancheva and D. Trushin for helpful discussions and suggestions. The work was partially supported by the Program of fundamental researches of the Presidium of RAS (P-28), the Russian Foundation for Basic Research (grants 18-07-01434, 18-29-22096, 19-07-01198).

This research has made use of the VizieR catalog access tool and the SIMBAD database operated at CDS, Strasbourg, France, the Washington Double Star Catalog maintained at the U.S. Naval Observatory, NASA’s Astrophysics Data System Bibliographic Services, Joint Supercomputer Center of the Russian Academy of Sciences, and data from the European Space Agency (ESA) mission Gaia (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).

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Malkov, O. et al. (2019). Evaluation of Binary Star Formation Models Using Well-Observed Visual Binaries. In: Manolopoulos, Y., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, vol 1003. Springer, Cham. https://doi.org/10.1007/978-3-030-23584-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-23584-0_6

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