Basic Testing of the Duchamp Source Finder

TL;DR

This study evaluates Duchamp, a source finder for spectroscopic data cubes, demonstrating its effectiveness in detecting and measuring sources, while highlighting systematic errors in parameter estimation caused by noise.

Contribution

The paper provides a comprehensive assessment of Duchamp's performance on various source models, offering insights into its strengths and limitations for astronomical data analysis.

Findings

Duchamp reliably detects sources at low signal-to-noise ratios.

Systematic errors affect measurements of spectral width and flux due to noise.

Duchamp's source parametrisation can be improved with secondary algorithms.

Abstract

This paper presents and discusses the results of basic source finding tests in three dimensions (using spectroscopic data cubes) with Duchamp, the standard source finder for the Australian SKA Pathfinder. For this purpose, we generated different sets of unresolved and extended HI model sources. These models were then fed into Duchamp, using a range of different parameters and methods provided by the software. The main aim of the tests was to study the performance of Duchamp on sources with different parameters and morphologies and assess the accuracy of Duchamp's source parametrisation. Overall, we find Duchamp to be a powerful source finder capable of reliably detecting sources down to low signal-to-noise ratios and accurately measuring their position and velocity. In the presence of noise in the data, Duchamp's measurements of basic source parameters, such as spectral line width and integrated flux, are affected by systematic errors. These errors are a consequence of the effect of noise on the specific algorithms used by Duchamp for measuring source parameters in combination with the fact that the software only takes into account pixels above a given flux threshold and hence misses part of the flux. In scientific applications of Duchamp these systematic errors would have to be corrected for. Alternatively, Duchamp could be used as a source finder only, and source parametrisation could be done in a second step using more sophisticated parametrisation algorithms.