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

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2018)

Part of the book series: Communications in Computer and Information Science ((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.

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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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Authors and Affiliations

  1. Institute of Astronomy of Russian Academy of Sciences, Moscow, Russia

    Oleg Malkov, Dmitry Chulkov, Dana Kovaleva, Alexey Sytov, Alexander Tutukov & Lev Yungelson

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

    Nikolay A. Skvortsov

  3. Entoto Observatory and Research Center, Addis Ababa, Ethiopia

    Yikdem Gebrehiwot

  4. College of Natural and Computational Sciences, Mekelle University, Mekelle, Ethiopia

    Yikdem Gebrehiwot

  5. Entoto Observatory and Research Center Astronomy and Astrophysics, Ethiopian Space Science and Technology Institute, Addis Ababa, Ethiopia

    Solomon Belay Tessema

Authors
  1. Oleg Malkov
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  2. Dmitry Chulkov
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  3. Yikdem Gebrehiwot
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  4. Dana Kovaleva
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  5. Nikolay A. Skvortsov
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  6. Alexey Sytov
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  8. Alexander Tutukov
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  9. Lev Yungelson
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Correspondence to Oleg Malkov .

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Editors and Affiliations

  1. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

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

    Sergey Stupnikov

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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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