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Data for Binary Stars from Gaia DR2

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1427))

Abstract

Gaia space mission has provided a large amount of astrometric and photometric data for the Milky Way stellar population. Binary or multiple systems form a significant part of it. In spite of the fact that Gaia DR2 treats all stars as singles in respect to astrometric solutions, it still can be used to enrich our knowledge of the binary population in several ways. Many stars known to be components of binaries have received new astrometric measurements of validity to be discussed. Tens of thousands of prospective binaries were discovered by different authors as co-moving stars situated in space close to each other. We combine and analyze the new information for binaries from Gaia DR2, and include it into the Identification List of Binaries (ILB), a complete list of known binary and multiple stars.

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Acknowledgement

The study was partly funded by RFBR, project numbers 19-07-01198, 18-07-01434. The use of TOPCAT tool http://www.starlink.ac.uk/topcat/ [24] is gratefully acknowledged. This work has made use of 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). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10.26093/cds/vizier). The original description of the VizieR service was published in 2000, A&AS 143, 23.

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Authors and Affiliations

  1. Institute of Astronomy, Moscow, 119017, Russia

    Dana Kovaleva, Oleg Malkov, Sergei Sapozhnikov & Dmitry Chulkov

  2. Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, 119333, Russia

    Nikolay Skvortsov

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  1. Dana Kovaleva
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  2. Oleg Malkov
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  3. Sergei Sapozhnikov
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  4. Dmitry Chulkov
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  5. Nikolay Skvortsov
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Correspondence to Dana Kovaleva .

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Editors and Affiliations

  1. Voronezh State University, Voronezh, Russia

    Alexander Sychev

  2. Voronezh State University, Voronezh, Russia

    Sergey Makhortov

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

    Bernhard Thalheim

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Kovaleva, D., Malkov, O., Sapozhnikov, S., Chulkov, D., Skvortsov, N. (2021). Data for Binary Stars from Gaia DR2. In: Sychev, A., Makhortov, S., Thalheim, B. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2020. Communications in Computer and Information Science, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-030-81200-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-81200-3_9

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  • Online ISBN: 978-3-030-81200-3

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