Radio Galaxy Zoo: compact and extended radio source classification with deep learning
Open accessAbstract
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. In the context of radio-interferometric imaging in astronomy, we looked for ways to identify multiple components of individual sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers, and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple-component extended sources. We found that a three-convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4 per cent on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test accuracy of 93.5 per cent. The best-performing convolutional neural network set-up has been verified against RGZ Data Release 1 where a final test accuracy of 94.8 per cent was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.
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Lukic et al. (2018) concluded that a three-layer network is typically enough to reach more than 90% accuracy.
“Lukic et al. (2018) explores different network configurations and concludes that a three layer network is typically enough to reach more than 90% accuracy.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Lukic et al. 2018
Section 4.0.1 (p. 8) reports test accuracy above 93% with two dense layers, so the paper does show that a simple configuration exceeded 90% on its two-class task. But the Conclusions (p. 14) identify the best-performing architecture as three convolutional plus two dense layers, not a three-layer network. The paper does not conclude that three layers are typically sufficient across tasks. (LLM, conf=0.89)
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Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN
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Segal, G., Parkinson, D., Norris, R. P., Hopkins, A., Andernach, H., Alexander, E., Carretti, E., Koribalski, B. S., Legodi, L. S., Leslie, S. K., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad537 · Cited by 13 · see citing works
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Stuardi, C., Gheller, C., Vazza, F., & Botteon, A. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2014 · Cited by 10 · see citing works
Riggi, S., Cecconello, T., Palazzo, S., Hopkins, A., Gupta, N., Bordiú, C., Ingallinera, A., Buemi, C. S., Bufano, F., Cavallaro, F., et al. (2024) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2024.84 · Cited by 10 · see citing works
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Mvita, M. J., Zulu, N. G., & Thethwayo, B. M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111457 · Cited by 9 · see citing works
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Deep Residual Networks Model for Star-Galaxy Classification
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Lao, B., Andernach, H., Yang, X., Zhang, X., Zhao, R.-S., Zhao, Z. W., Yu, Y., Sun, X., & Qin, S. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad9c6d · Cited by 8 · see citing works
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Morphological-based Analyses for Parameterizing Symmetry in Radio Galaxies
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Radio Galaxy Zoo: Tagging radio subjects using text
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Self-citationMorphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods
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Self-citationThe Complexity and Information Content of Simulated Universes
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RGC: A radio AGN classifier based on deep learning
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- Authors
- Lukic, V, Brüggen, M., Banfield, J. K., Wong, O. I., Rudnick, L., Norris, R. P., & Simmons, B.
- Affiliations
- Universität Hamburg (DE)
- Australian National University (AU)
- The University of Sydney (AU)
- University of Minnesota (US)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- University of California San Diego (US)
- Corresponding author
- Lukic, V
- Brüggen, M
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-01-26
- Volume
- 476
- Issue
- 1
- Pages
- 246-260
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2782681900
- Cited by
- 86
- References
- 35
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 13 · 2022: 13 · 2021: 16 · 2020: 8 · 2019: 13 · 2018: 7
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- Lancaster EPrints (Lancaster University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Science and Technology Facilities Council (SFB 676)
- University of Minnesota (AST-1211595, AST-1714205)
- National Aeronautics and Space Administration (PF5-160143)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Radio Astronomy Observations and Technology
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysical Phenomena and Observations
- Keywords
- radio galaxy morphology, compact radio sources, extended radio sources, convolutional neural networks, citizen science, radio interferometric imaging, image augmentation, hyperparameter optimization, training data size
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