{
  "abstract": "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 944 MHz, with rms ~ 30 uJy/beam over 270 deg2 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 15 (recall 96 %) up to z ~ 5, and by nearly a factor of 100 (recall 94 %) for z > 1, respectively. In extrapolating these numbers for the full EMU survey, we expect to recover more than 3.4 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 (800-1088 MHz) spectral indices. 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.06190",
  "author_total": 16,
  "authors": [
    {
      "name": "R. Carvajal"
    },
    {
      "name": "I. Matute"
    },
    {
      "name": "J. Afonso"
    },
    {
      "name": "H. Messias"
    },
    {
      "name": "M. J. I. Brown"
    },
    {
      "name": "D. Farrah"
    },
    {
      "name": "A. M. Hopkins"
    },
    {
      "name": "J. Th. van Loon"
    },
    {
      "name": "K. J. Luken"
    },
    {
      "name": "C. M. Pennock"
    },
    {
      "name": "J. Prathap"
    },
    {
      "name": "S. S. Shabala"
    },
    {
      "name": "D. A. Leahy"
    },
    {
      "name": "S. F. Rahman"
    },
    {
      "name": "K. A. Pimbblet"
    },
    {
      "name": "T. Zafar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/74zgdspet11a9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06190",
  "fulltext_url": null,
  "id": "74zgdspet11a9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06190",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06190",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/74zgdspet11a9.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:20:56Z",
      "native_id": "2611.06190",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06190"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    }
  ],
  "title": "Evolutionary Map of the Universe (EMU): Selection of active galactic nuclei and star-formation emission with machine-learning techniques",
  "title_text": "Evolutionary Map of the Universe (EMU): Selection of active galactic nuclei and star-formation emission with machine-learning techniques",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:20Z",
  "url": "https://research.draconitic.com/paper/74zgdspet11a9",
  "venue": "arXiv",
  "versions": []
}