Abstract Extended radio sources present unique challenges for automated detection and classification in wide-field radio surveys. With current surveys such as the Evolutionary Map of the Universe (EMU), robust and scalable methods are essential to identify and catalogue these complex sources. We apply three automatic approaches to detect complex radio emission in EMU observations of the Galaxy And Mass Assembly (GAMA) 09 field (EMU-G09) in order to evaluate their relative strengths and limitations in preparation for large-scale application across future EMU data releases. These include DRAGN HUNTER , designed to detect likely DRAGNs (Double Radio sources associated with Active Galactic Nuclei) from a component catalogue; coarse-grained complexity, a metric designed to highlight regions of complex emission; and RG-CAT, a machine learning pipeline trained on radio sources identified in the EMU pilot survey. We find that together, the three methods recover nearly all extended sources in EMU-G09 but identify largely distinct, partially-overlapping subsets, with only 375 sources identified by all finders. This demonstrates that a combination of complementary techniques will be required to achieve a complete census of extended radio sources in future large-scale surveys.
Barnes et al. (Mon,) studied this question.