{"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/using-cgans-for-anomaly-detection-identifying","title":"Using cGANs for Anomaly Detection: Identifying Astronomical Anomalies in JWST NIRcam Imaging","arxiv_id":"2310.09073","date":"2023-10-13","proceeding":null,"authors":["Ruby Pearce-Casey","Hugh Dickinson","Stephen Serjeant","Jane Bromley"],"abstract":"We present a proof of concept for mining JWST imaging data for anomalous galaxy populations using a conditional Generative Adversarial Network (cGAN). We train our model to predict long wavelength NIRcam fluxes (LW: F277W, F356W, F444W between 2.4 to 5.0\\mu m) from short wavelength fluxes (SW: F115W, F150W, F200W between 0.6 to 2.3\\mu m) in approximately 2000 galaxies. We test the cGAN on a population of 37 Extremely Red Objects (EROs) discovered by the CEERS JWST Team arXiv:2305.14418. Despite their red long wavelength colours, the EROs have blue short wavelength colours (F150W \\- F200W equivalently 0 mag) indicative of bimodal SEDs. Surprisingly, given their unusual SEDs, we find that the cGAN accurately predicts the LW NIRcam fluxes of the EROs. However, it fails to predict LW fluxes for other rare astronomical objects, such as a merger between two galaxies, suggesting that the cGAN can be used to detect some anomalies","url_abs":"https://arxiv.org/abs/2310.09073v1","url_pdf":"https://arxiv.org/pdf/2310.09073v1.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":"using-cgans-for-anomaly-detection-identifying","repo_url":"https://github.com/rubypc/anomaly_detection_with_cgans","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}