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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 \({\sim }\) 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.

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Radio Galaxy Zoo: EMU—Paving the Way for EMU Cataloging Using AI and Citizen Science

  • 会议论文

  • 发表日期：

    2026年

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  • Hongming Tang,
  • Eleni Vardoulaki,
  • Micah Bowles,
  • Gary Segal,
  • Soheb Mandhai,
  • Emma L. Alexander,
  • Wendy Williams,
  • Yan Luo,
  • Lawrence Rudnick,
  • Andrew M. Hopkins,
  • O. Ivy Wong,
  • Stanislav S. Shabala

摘要

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 \({\sim }\) 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.