RGZ EMU user interface and its 3-step workflow setup. The first 2 steps (in blue color) of
the workflow is compulsary, while the third (in green color) is optional. — astro-ph.IM
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 around 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.
A schematic diagram showing the proposed RGZ EMU cataloging framework using citizen science and machine learning. The blue box (top left panel) shows sample images that are excluded from the final selection, as having either a low complexity or a source major axis smaller than 20 arcsec. Sample images in the red box, on the contrary, refers to sample images fulfilled our selection criteria. Gradient blue arrows refers to operation done within the framework, and gradient green arrows indicates the framework components that will contribute to the EMUCAT supplementary catalogs. — astro-ph.IM
Hongming Tang, Eleni Vardoulaki, RGZ EMU collaboration
Comments: 6 pages, 2 figures, The 2nd edition of the International Conference on Machine Learning for Astrophysics (ML4ASTRO2), conference paper accepted
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2506.16138 [astro-ph.IM] (or arXiv:2506.16138v1 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2506.16138
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Submission history
From: Hongming Tang
[v1] Thu, 19 Jun 2025 08:44:47 UTC (7,722 KB)
https://arxiv.org/abs/2506.16138
Astrobiology,
