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Computational Intelligence in Astronomy – A Win-Win Situation

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Theory and Practice of Natural Computing (TPNC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7505))

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Abstract

Large archives of astronomical data (images, spectra and catalogues of derived parameters) are being assembled worldwide as part of the Virtual Observatory project. In order for such massive heterogeneous data collections to be of use to astronomers, development of Computational Intelligence techniques that would combine modern machine learning with deep domain knowledge is crucial. Both fields - Computer Science and Astronomy - can hugely benefit from such a research program. Astronomers can gain new insights into structures buried deeply in the data collections that would, without the help of Computational Intelligence, stay masked. On the other hand, computer scientists can get inspiration and motivation for development of new techniques driven by the specific characteristics of astronomical data and the need to include domain knowledge in a fundamental way. In this review we present three diverse examples of such successful symbiosis.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Peter Tiňo

  2. School of Physics and Astronomy, University of Birmingham, Birmingham, B15 2TT, UK

    Somak Raychaudhury

Authors
  1. Peter Tiňo
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  2. Somak Raychaudhury
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Editor information

Editors and Affiliations

  1. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Avinguda Catalunya, 35, 43002, Tarragona, Spain

    Adrian-Horia Dediu  & Carlos Martín-Vide  & 

  2. Fakultät für Informatik, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Bianca Truthe

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Tiňo, P., Raychaudhury, S. (2012). Computational Intelligence in Astronomy – A Win-Win Situation. In: Dediu, AH., Martín-Vide, C., Truthe, B. (eds) Theory and Practice of Natural Computing. TPNC 2012. Lecture Notes in Computer Science, vol 7505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33860-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-33860-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33859-5

  • Online ISBN: 978-3-642-33860-1

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