Source-Finding for the Australian Square Kilometre Array Pathfinder
M. Whiting A B and B. Humphreys AA CSIRO Astronomy and Space Science, PO Box 76, Epping, NSW 1710, Australia
B Corresponding author. Email: matthew.whiting@csiro.au
Publications of the Astronomical Society of Australia 29(3) 371-381 https://doi.org/10.1071/AS12028
Submitted: 30 March 2012 Accepted: 10 August 2012 Published: 30 August 2012
Abstract
The Australian Square Kilometre Array Pathfinder (ASKAP) presents a number of challenges in the area of source finding and cataloguing. The data rates and image sizes are very large, and require automated processing in a high-performance computing environment. This requires development of new tools, that are able to operate in such an environment and can reliably handle large datasets. These tools must also be able to accommodate the different types of observations ASKAP will make: continuum imaging, spectral-line imaging, transient imaging. The ASKAP project has developed a source-finder known as selavy, built upon the duchamp source-finder. selavy incorporates a number of new features, which we describe here.
Since distributed processing of large images and cubes will be essential, we describe the algorithms used to distribute the data, find an appropriate threshold and search to that threshold and form the final source catalogue. We describe the algorithm used to define a varying threshold that responds to the local, rather than global, noise conditions, and provide examples of its use. And we discuss the approach used to apply two-dimensional fits to detected sources, enabling more accurate parameterisation. These new features are compared for timing performance, where we show that their impact on the pipeline processing will be small, providing room for enhanced algorithms.
We also discuss the development process for ASKAP source finding software. By the time of ASKAP operations, the ASKAP science community, through the Survey Science Projects, will have contributed important elements of the source finding pipeline, and the mechanisms in which this will be done are presented.
Keywords: methods: data analysis — methods: numerical — techniques: image processing
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