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

Radio Galaxy Zoo: EMU collaboration

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

Radio Galaxy Zoo: EMU collaboration

Department of Physics

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 language	English
Number of pages	6
Publication status	E-pub ahead of print - 19 Jun 2025
Event	The 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

Conference	The 2nd edition of the International Conference on Machine Learning for Astrophysics
Abbreviated title	ML4ASTRO2
Country/Territory	Italy
City	Catania
Period	8/07/24 → 14/07/24
Internet address	https://indico.ict.inaf.it/event/2690/overview

1 Active

SKA-XAI: Trusted AI for Radio Astrophysics
Tang, H. (PI)
1/07/25 → 30/06/28
Project: Internal Research Project

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
7 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
9 Citations (Scopus)

Cite this

@conference{a29363506ffb440587aa822606eccaba,

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

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 \textasciitilde{}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.",

author = "Hongming Tang and Eleni Vardoulaki and Micah Bowles and Gary Segal and Soheb Gandhi and Alexander, \{Emma L.\} and Wendy Williams and Yan Luo and Lawrence Rudnick and Hopkins, \{Andrew M.\} and Wong, \{O. Ivy\} and Shabala, \{Stanislav S.\} and \{Radio Galaxy Zoo: EMU collaboration\}",

year = "2025",

month = jun,

day = "19",

language = "English",

note = "The 2nd edition of the International Conference on Machine Learning for Astrophysics, ML4ASTRO2 ; Conference date: 08-07-2024 Through 14-07-2024",

url = "https://indico.ict.inaf.it/event/2690/overview",

}

TY - CONF

T1 - Radio Galaxy Zoo: EMU - paving the way for EMU cataloging using AI and citizen science

AU - Tang, Hongming

AU - Vardoulaki, Eleni

AU - Bowles, Micah

AU - Segal, Gary

AU - Gandhi, Soheb

AU - Alexander, Emma L.

AU - Williams, Wendy

AU - Luo, Yan

AU - Rudnick, Lawrence

AU - Hopkins, Andrew M.

AU - Wong, O. Ivy

AU - Shabala, Stanislav S.

AU - Radio Galaxy Zoo: EMU collaboration

PY - 2025/6/19

Y1 - 2025/6/19

N2 - 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.

AB - 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.

M3 - Paper

T2 - The 2nd edition of the International Conference on Machine Learning for Astrophysics

Y2 - 8 July 2024 through 14 July 2024

ER -

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

Abstract

Conference

Fingerprint

Projects

SKA-XAI: Trusted AI for Radio Astrophysics

Research output

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

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

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

Cite this