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    An Improved Statistical Point-source Foreground Model for the Epoch of Reionization

    Access Status
    Fulltext not available
    Authors
    Murray, Steven
    Trott, Cathryn
    Jordan, C.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Murray, S. and Trott, C. and Jordan, C. 2017. An Improved Statistical Point-source Foreground Model for the Epoch of Reionization. Astrophysical Journal. 845 (1).
    Source Title
    Astrophysical Journal
    DOI
    10.3847/1538-4357/aa7d0a
    ISSN
    0004-637X
    School
    Curtin Institute of Radio Astronomy (Physics)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DE140100316
    URI
    http://hdl.handle.net/20.500.11937/57761
    Collection
    • Curtin Research Publications
    Abstract

    © 2017. The American Astronomical Society. All rights reserved.. We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions for the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.

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