EMUSE (the EMU search engine) uses machine learning and AI to help classify different types of radio sources into categories.

The latest issue of the ATNF News has just been published. This issue includes Notes from the ATNF leadership team, Learnings from the ATCA Science Day in April, an update on BIGCAT commissioning, an introduction to the new Australia Telescope Users Committee (ATUC) and Time Assignment Committee (TAC) executive officers, and an article on the management of radio-frequency interference (RFI). The image above is one of the science highlights included in the issue: it shows the output of the EMU Search Engine (EMUSE), developed to identify galaxies with rare or complex shapes without manually browsing thousands of images. When a screenshot of an FR-II radio galaxy (a radio galaxy classification) was uploaded using the image upload option in the EMUSE app, it output a catalogue of similar FR-II radio galaxies from the EMU survey. The top 25 similar sources are plotted here.