Evolutionary Map of the Universe (EMU): Selection of active galactic nuclei and star-formation emission with machine-learning techniques

Sources detected by recent large-area radio surveys require robust classification and characterisation. The Evolutionary Map of the Universe (EMU) is a prime example of machine-learning (ML) techniques efficiently enriching existing catalogues with classifications and derived physical properties that would be impractical to obtain from spectroscopy or colour selection alone. We aim to build a labelled sample of sources in the EMU pilot survey (EMU-PS, at qty 944 , with rms ∼ over qty 30 270 in the southern sky) classified as being dominated by active galactic nuclei (AGNs) or non-AGN processes including star-formation. We will then use this sample to examine radio properties and redshift-dependent trends. We applied a pre-trained multi-stage ML pipeline to optical-to-infrared photometry of EMU-PS sources to derive classifications and photometric redshifts, which we used to compute additional radio properties, including luminosities. We increase the known number of AGNs and non-AGNs in the EMU-PS by more than a factor of ) up to z ∼ , and by nearly a factor of ) for z łesssim , respectively. In extrapolating these numbers for the full EMU survey, we expect to recover more than ) spectral indices. (recall 96 5 (recall 94 1 million AGN candidates. A non-negligible low-redshift subsample of selected non-AGNs shows radio luminosities in excess of those expected from star formation alone, revealing detachment between photometric and radio classification. Radio-derived properties show expected trends for luminosities and a lack of evolution for in-band (qtyrange range-units=single, range-phrase= -- 800 1088 The ML-based pipeline provides a scalable way to produce large catalogues for the ongoing EMU survey and future Square Kilometre Array observations and reveals that multi-wavelength analysis can uncover radio populations and trends often missed by colour cuts or spectroscopy alone.