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    The ASKAP/EMU Source Finding Data Challenge

    Access Status
    Open access via publisher
    Authors
    Hopkins, A.
    Whiting, M.
    Seymour, Nick
    Chow, K.
    Norris, R.
    Bonavera, L.
    Breton, R.
    Carbone, D.
    Ferrari, C.
    Franzen, Thomas
    Garsden, H.
    González-Nuevo, J.
    Hales, C.
    Hancock, Paul
    Heald, G.
    Herranz, D.
    Huynh, M.
    Jurek, R.
    López-Caniego, M.
    Massardi, M.
    Mohan, N.
    Molinari, S.
    Orrù, E.
    Paladino, R.
    Pestalozzi, M.
    Pizzo, R.
    Rafferty, D.
    Röttgering, H.
    Rudnick, L.
    Schisano, E.
    Shulevski, A.
    Swinbank, J.
    Taylor, R.
    van der Horst, A.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Hopkins, A. and Whiting, M. and Seymour, N. and Chow, K. and Norris, R. and Bonavera, L. and Breton, R. et al. 2015. The ASKAP/EMU Source Finding Data Challenge. Publications of the Astronomical Society of Australia.
    Source Title
    Publications of the Astronomical Society of Australia
    DOI
    10.1017/pasa.2015.37
    ISSN
    1323-3580
    School
    Department of Physics and Astronomy
    URI
    http://hdl.handle.net/20.500.11937/4131
    Collection
    • Curtin Research Publications
    Abstract

    The Evolutionary Map of the Universe (EMU) is a proposed radio continuum survey of the Southern Hemisphere up to declination + 30°, with the Australian Square Kilometre Array Pathfinder (ASKAP). EMU will use an automated source identification and measurement approach that is demonstrably optimal, to maximise the reliability and robustness of the resulting radio source catalogues. As a step toward this goal we conducted a “Data Challenge” to test a variety of source finders on simulated images. The aim is to quantify the accuracy and limitations of existing automated source finding and measurement approaches. The Challenge initiators also tested the current ASKAPsoft source-finding tool to establish how it could benefit from incorporating successful features of the other tools. As expected, most finders show completeness around 100% at ≈ 10σ dropping to about 10% by ≈ 5σ. Reliability is typically close to 100% at ≈ 10σ, with performance to lower sensitivities varying between finders. All finders show the expected trade-off, where a high completeness at low signal-to-noise gives a corresponding reduction in reliability, and vice versa. We conclude with a series of recommendations for improving the performance of the ASKAPsoft source-finding tool.

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