Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey

Part of the Springer Series in Astrostatistics book series (SSIA, volume 2)


The Next Generation Virgo Cluster Survey is a 104-square-degree survey of the Virgo Cluster, carried out using the MegaPrime camera of the Canada–France–Hawaii Telescope, from semesters 2009A–2012A. The survey will provide coverage of this nearby dense environment in the universe to unprecedented depth, providing profound insights into Galaxy formation and evolution, including definitive measurements of the properties of galaxies in a dense environment in the local universe, such as the luminosity function. The limiting magnitude of the survey is g AB = 25.7 (10σ point source), and the 2σ surface brightness limit is g AB ≈ 29 mag arcsec−2. The data volume of the survey (approximately 50 T of images), while large by contemporary astronomical standards, is not intractable. This renders the survey amenable to the methods of astroinformatics. The enormous dynamic range of objects, from the giant elliptical Galaxy M87 at \(M(B) = -21.6\) to the faintest dwarf ellipticals at M(B)≈−6, combined with photometry in five broad bands (u* griz’) and unprecedented depth revealing many previously unseen structures, creates new challenges in object detection and classification. We present results from ongoing work on the survey, including photometric redshifts, Virgo cluster membership, and the implementation of fast data mining algorithms on the infrastructure of the Canadian Astronomy Data Centre, as part of the Canadian Advanced Network for Astronomical Research.


  1. 1.
    Ball NM, Brunner RJ (2010) Data mining and machine learning in astronomy. Int J Mod Phys D 19:1049–1106ADSMATHCrossRefGoogle Scholar
  2. 2.
    Bertin E, Mellier Y, Radovich M, et al (2002) The TERAPIX pipeline. In: Bohlender DA, Durand D, Handley TH (eds) Astronomical data analysis software and systems XI, ASP Conference Proceeding, vol 281. Astronomical Society of the Pacific, San Francisco, pp 228–237Google Scholar
  3. 3.
    Binggeli B, Sandage A, Tammann GA (1985) Studies of the Virgo cluster. II. A catalog of 2096 galaxies in the Virgo cluster area. Astron J 90:1681–1758Google Scholar
  4. 4.
    Borne K (2009) Scientific data mining in astronomy. Data mining and knowledge discovery series. Taylor & Francis/CRC Press, Boca Raton, FL, Ch. 5, pp 91114Google Scholar
  5. 5.
    Gaudet S, Hill N, Armstrong P, et al. CANFAR: the Canadian advanced network for astronomical research. In: Radziwill NM, Bridger A (eds) Proceedings of SPIE, Software and Cyberinfrastructure in Astronomy, vol 7740, 1LGoogle Scholar
  6. 6.
    Gwyn SDJ (2008) MegaPipe: the MegaCam image stacking pipeline at the Canadian astronomical data centre. Publ Astron Soc Pac 120:212–223ADSCrossRefGoogle Scholar
  7. 7.
    Gwyn SDJ (2011) The CFHT legacy survey: stacked images and catalogs. arXiv/1101.1084Google Scholar
  8. 8.
    Schlegel DJ, Finkbeiner DP, Davis M (1998) Maps of dust infrared emission for use in estimation of reddening and cosmic microwave background radiation foregrounds. Astrophys J 500:525–553ADSCrossRefGoogle Scholar
  9. 9.
    Taylor MB (2006) STILTS — A package for command-line processing of tabular data. In: Gabriel C, et al (eds) Astronomical data analysis software and systems XV, ASP Conference Proceeding, vol 351. Astronomical Society of the Pacific, San Francisco, pp 666–669Google Scholar
  10. 10.
    York D, Adelman J, Anderson JE, et al (2000) The sloan digital sky survey: technical summary. Astron J 120:1579–1587ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.National Research Council Herzberg Institute of AstrophysicsVictoriaCanada

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