Minh Huynh, Andrew Hopkins, Ray Norris, Paul Hancock, Tara Murphy, Russell Jurek, Matthew Whiting
The process of determining the number and characteristics of sources in
astronomical images is so fundamental to a large range of astronomical problems
that it is perhaps surprising that no standard procedure has ever been defined
that has well understood properties with a high degree of statistical rigour on
completeness and reliability. There are now a large number of commonly used
software tools for accomplishing this task, typically with different tools
being used for images acquired using different technologies. Despite this,
there have been relatively few quantitative analyses of the robustness or
reliability of individual tools, or the details of the techniques they
implement. We have an opportunity to redress this omission in the context of
surveys planned with the Australian Square Kilometre Array Pathfinder (ASKAP).
The Evolutionary Map of the Universe (EMU) survey with ASKAP, a continuum
survey of the Southern Hemisphere up to declination +30 deg, aims to utilise an
automated source identification and measurement approach that is demonstrably
optimal, to maximise the reliability, utility and robustness of the resulting
radio source catalogues. A key stage in source extraction methods is the
background estimation (background level and noise level) and the choice of a
threshold high enough to reject false sources yet not so high that the
catalogues are significantly incomplete. In this analysis we present results
from testing such algorithms as implemented in the SExtractor, Selavy
(Duchamp), and sfind tools on simulated data. In particular the effects of
background estimation, threshold and false-discovery rate settings are
explored. For parameters that give similar completeness, the false-discovery
rate method employed by sfind results in a more reliable catalogue compared to
the peak threshold methods of SExtractor and Selavy.
View original:
http://arxiv.org/abs/1112.1168
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