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Radio Galaxy Zoo: EMU - paving the way for EMU cataloging using AI and citizen science

  • Radio Galaxy Zoo: EMU collaboration
  • National Observatory of Athens
  • department of astronomy, oxford university
  • University of Queensland
  • University of Manchester
  • University of Leeds
  • SKA Observatory
  • Department of Astronomy, School of Physics, Peking University
  • Minnesota Institute for Astrophysics, University of Minnesota
  • School of Mathematical and Physical Sciences, Macquarie University
  • CSIRO Space and Astronomy, ATNF
  • School of Physical Sciences, University of Tasmania

Research output: Contribution to conference › Paper › peer-review

Abstract

The Evolutionary Map of the Universe (EMU) survey with ASKAP is transforming our understanding of radio galaxies, AGN duty cycles, and cosmic structure. EMUCAT efficiently identifies compact radio sources, yet struggles with extended objects, requiring alternative approaches. The Radio Galaxy Zoo: EMU (RGZ EMU) project proposes a general framework that combines citizen science and machine learning to identify ~4 million extended sources in EMU. This framework is expected to enhance the EMUCAT cataloging on extended sources and can be further empowered with the introduction of cross-matched external data from surveys such as POSSUM and WALLABY.
Original languageEnglish
Number of pages6
Publication statusPublished - 1 Apr 2026
EventThe 2nd edition of the International Conference on Machine Learning for Astrophysics - Università degli Studi di Catania - Dipartimento di Fisica e Astronomia Via S. Sofia, 64, 95123 Catania CT, Catania, Italy
Duration: 8 Jul 2024 → 14 Jul 2024
https://indico.ict.inaf.it/event/2690/overview

Conference

ConferenceThe 2nd edition of the International Conference on Machine Learning for Astrophysics
Abbreviated titleML4ASTRO2
Country/TerritoryItaly
CityCatania
Period8/07/24 → 14/07/24
Internet address

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  • 2 Article
  • 1 Conference Proceeding

  • A model local interpretation routine for deep learning based radio galaxy classification

    Tang, H., Yue, S., Wang, Z., Lai, J., Wei, L., Luo, Y., Liang, C., Chu, J. & Xu, D., 2023, 2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023. Institute of Electrical and Electronics Engineers Inc., (2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023).

    Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

    2 Citations (Scopus)

  • Identifying anomalous radio sources in the Evolutionary Map of the Universe Pilot Survey using a complexity-based approach

    Segal, G., Parkinson, D., Norris, R., Hopkins, A. M., Andernach, H., Alexander, E. L., Carretti, E., Koribalski, B. S., Legodi, L. S., Leslie, S., Luo, Y., Pierce, J. C. S., Tang, H., Vardoulaki, E. & Vernstrom, T., 1 May 2023, In: Monthly Notices of the Royal Astronomical Society. 521, 1, p. 1429-1447 19 p.

    Research output: Contribution to journal › Article › peer-review

    Open Access
    10 Citations (Scopus)

  • Radio galaxy zoo EMU: Towards a semantic radio galaxy morphology taxonomy

    Bowles, M., Tang, H., Vardoulaki, E., Alexander, E. L., Luo, Y., Rudnick, L., Walmsley, M., Porter, F., Scaife, A. M. M., Slijepcevic, I. V., Adams, E. A. K., Drabent, A., Dugdale, T., Gürkan, G., Hopkins, A. M., Jimenez-Andrade, E. F., Leahy, D. A., Norris, R. P., ur Rahman, S. F. & Ouyang, X. & 3 others, Segal, G., Shabala, S. S. & Wong, O. I., 1 Jun 2023, In: Monthly Notices of the Royal Astronomical Society. 522, 2, p. 2584-2600 17 p.

    Research output: Contribution to journal › Article › peer-review

    Open Access
    15 Citations (Scopus)

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