Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks
Open accessAbstract
The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalogue with the WGAN anomaly scores; the code and catalogue are available at https://github.com/kstoreyf/anomalies-GAN-HSC; and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.
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Storey-Fisher et al. (2021) trained a WGAN using postage stamps of observed galaxies from the HSC survey to generate realistic images of galaxies.
“Storey-Fisher et al. (2021) trained a Generative Adversarial Network using postage stamps of observed galaxies from the Hyper Suprime Cam (HSC) survey.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Storey-Fisher et al. (2021)
A significant fraction of the identified objects are only image artefacts.
“However, a significant fraction of them are only image artefacts.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Storey-Fisher et al. (2021)
After adding a CAE layer trained on residual images, different types of anomalies cluster together.
“They show that, after this additional step, the different types of anomalies cluster together and a visual inspection is proposed to identify the most interesting anomalies (Figure 37).”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Storey-Fisher et al. (2021)
The WGAN is able to find more subtle anomalies because of the improved quality of the reconstruction.
“The WGAN is able to find more subtle anomalies because of the improved quality of the reconstruction.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Storey-Fisher et al. (2021)
The framework of Storey-Fisher et al. (2021) is able to identify potentially interesting objects.
“They show that the framework is able to identify potentially interesting objects.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Storey-Fisher et al. (2021)
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“Storey-Fisher et al. (2021) trained a Generative Adversarial Network using postage stamps of observed galaxies from the Hyper Suprime Cam (HSC) survey.”
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Vago, N. O. P., Martínez-Galarza, J. R., & Amato, R. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202557384 · Cited by 1 · see citing works
Detecting pattern irregularities in astronomical images using deep learning
Shankar, T., Deekshitha, B., Rohini, V., Nakarajan, V. M., Sivasankar, P. T., & Murali, J. A. (2024) · International Conference on Artificial Intelligence for Internet of Things (AIIoT) · doi:10.1109/aiiot58432.2024.10574579 · Cited by 1 · see citing works
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Spectrum-Agnostic Space Debris Detection with Autoencoders
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- Authors
- Storey-Fisher, K., Huertas-Company, M., Ramachandra, N., Lanusse, F., Leauthaud, A., Luo, Y., Huang, S., & Prochaska, J. X.
- Affiliations
- New York University (US)
- Instituto de Astrofísica de Canarias (IAC); Departamento de Astrofísica, Universidad de La Laguna (ULL) , E-38200 La Laguna, Spain
- Argonne National Laboratory (US)
- AIM, CEA, CNRS, Universite Paris-Saclay, Université de Paris , F-91191 Gif-sur-Yvette, France
- Centre National de la Recherche Scientifique (FR)
- University of California, Santa Cruz (US)
- Kavli Institute for the Physics and Mathematics of the Universe (JP)
- Corresponding author
- Storey-Fisher, Kate
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-09-13
- Volume
- 508
- Issue
- 2
- Pages
- 2946-2963
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W3158374188
- Cited by
- 38
- References
- 47
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 13 · 2023: 7 · 2022: 9 · 2021: 2
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of California
- Kavli Foundation
- National Science Foundation (1714610)
- U.S. Department of Energy
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Gamma-ray bursts and supernovae
- Keywords
- anomaly detection, galaxy images, Wasserstein GAN, Hyper Suprime-Cam, unsupervised anomaly detection, galaxy mergers, tidal features, star-forming galaxies, metal-poor dwarf galaxies, H II regions, convolutional autoencoder, UMAP clustering, astronomical surveys
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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