Source-finding for the Australian Square Kilometre Array Pathfinder

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 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.