Examining alternatives to wavelet de-noising for astronomical source finding

TL;DR

This paper compares various pre-processing techniques, including wavelet de-noising and median smoothing, to improve automated source finding in large astronomical datasets, highlighting iterative median smoothing as most effective.

Contribution

It evaluates and compares the effectiveness of different pre-processing methods for spectral line source finding, introducing implementations of iterative median smoothing and morphological subtraction.

Findings

Iterative median smoothing yields the best results for ASKAP HI data.

Wavelet de-noising is a more robust pre-processing method.

Pre-processing improves source detection performance.

Abstract

The Square Kilometre Array and its pathfinders ASKAP and MeerKAT will produce prodigious amounts of data that necessitate automated source finding. The performance of automated source finders can be improved by pre-processing a dataset. In preparation for the WALLABY and DINGO surveys, we have used a test HI datacube constructed from actual Westerbork Telescope noise and WHISP HI galaxies to test the real world improvement of linear smoothing, the {\sc Duchamp} source finder's wavelet de-noising, iterative median smoothing and mathematical morphology subtraction, on intensity threshold source finding of spectral line datasets. To compare these pre-processing methods we have generated completeness-reliability performance curves for each method and a range of input parameters. We find that iterative median smoothing produces the best source finding results for ASKAP HI spectral line observations, but wavelet de-noising is a safer pre-processing technique. In this paper we also present our implementations of iterative median smoothing and mathematical morphology subtraction.