Source-finding for the Australian Square Kilometre Array Pathfinder
Matthew Whiting, Ben Humphreys

TL;DR
This paper introduces Selavy, an advanced source-finding tool for ASKAP data, capable of handling large datasets with adaptive algorithms for improved accuracy and efficiency in various observation types.
Contribution
Development of Selavy, a new source-finder with algorithms for distributed processing, local noise adaptation, and detailed source parameterization tailored for ASKAP's large-scale data.
Findings
Selavy efficiently processes large datasets with minimal impact on timing performance.
The local noise thresholding improves source detection accuracy.
Two-dimensional source fitting enhances parameter estimation.
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 (Whiting 2012). 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…
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