We present a comparison between the performance of a selection of source finders using a new software tool called Hydra. The companion paper, Paper I, introduced the Hydra tool and demonstrated its performance using simulated data. Here we apply Hydra to assess the performance of different source finders by analysing real observational data taken from the Evolutionary Map of the Universe (EMU) Pilot Survey. EMU is a wide-field radio continuum survey whose primary goal is to make a deep (20μJy/beam RMS noise), intermediate angular resolution (15″), 1 GHz survey of the entire sky south of +30° declination, and expecting to detect and catalogue up to 40 million sources. With the main EMU survey expected to begin in 2022 it is highly desirable to understand the performance of radio image source finder software and to identify an approach that optimises source detection capabilities. Hydra has been developed to refine this process, as well as to deliver a range of metrics and source finding data products from multiple source finders. We present the performance of the five source finders tested here in terms of their completeness and reliability statistics, their flux density and source size measurements, and an exploration of case studies to highlight finder-specific limitations.

The latest generation of radio surveys are now producing sky survey images containing many millions of radio sources. In this context it is highly desirable to understand the performance of radio image source finder (SF) software and to identify an approach that optimises source detection capabilities. We have created Hydra to be an extensible multi-SF and cataloguing tool that can be used to compare and evaluate different SFs. Hydra, which currently includes the SFs Aegean, Caesar, ProFound, PyBDSF, and Selavy, provides for the addition of new SFs through containerisation and configuration files. The SF input RMS noise and island parameters are optimised to a 90% “percentage real detections” threshold (calculated from the difference between detections in the real and inverted images), to enable comparison between SFs. Hydra provides completeness and reliability diagnostics through observed-deep (D) and generated-shallow (S) images, as well as other statistics. In addition, it has a visual inspection tool for comparing residual images through various selection filters, such as S/N bins in completeness or reliability. The tool allows the user to easily compare and evaluate different SFs in order to choose their desired SF, or a combination thereof. This paper is part one of a two part series. In this paper we introduce the Hydra software suite and validate its D/S metrics using simulated data. The companion paper demonstrates the utility of Hydra by comparing the performance of SFs using both simulated and real images.