{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/anomaly-detection-in-astronomical-images-with","title":"Anomaly Detection in Astronomical Images with Generative Adversarial Networks","arxiv_id":"2012.08082","date":"2020-12-15","proceeding":null,"authors":["Kate Storey-Fisher","Marc Huertas-Company","Nesar Ramachandra","Francois Lanusse","Alexie Leauthaud","Yifei Luo","Song Huang"],"abstract":"We present an anomaly detection method using Wasserstein generative adversarial networks (WGANs) on optical galaxy images from the wide-field survey conducted with the Hyper Suprime-Cam (HSC) on the Subaru Telescope in Hawai'i. The WGAN is trained on the entire sample, and learns to generate realistic HSC-like images that follow the distribution of the training data. We identify images which are less well-represented in the generator's latent space, and which the discriminator flags as less realistic; these are thus anomalous with respect to the rest of the data. We propose a new approach to characterize these anomalies based on a convolutional autoencoder (CAE) to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images. We construct a subsample of ~9,000 highly anomalous images from our nearly million object sample, and further identify interesting anomalies within these; these include galaxy mergers, tidal features, and extreme star-forming galaxies. The proposed approach could boost unsupervised discovery in the era of big data astrophysics.","url_abs":"https://arxiv.org/abs/2012.08082v1","url_pdf":"https://arxiv.org/pdf/2012.08082v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"anomaly-detection-in-astronomical-images-with","repo_url":"https://github.com/kstoreyf/anomalies-GAN-HSC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2012.08082","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}