{"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/identification-of-galaxy-shreds-in-large","title":"Identification of galaxy shreds in large photometric catalogs using Convolutional Neural Networks","arxiv_id":"2301.09648","date":"2023-01-23","proceeding":null,"authors":["Enrico M. Di Teodoro","Josh E. G. Peek","John F. Wu"],"abstract":"Contamination from galaxy fragments, identified as sources, is a major issue in large photometric galaxy catalogs. In this paper, we prove that this problem can be easily addressed with computer vision techniques. We use image cutouts to train a convolutional neural network (CNN) to identify catalogued sources that are in reality just star formation regions and/or shreds of larger galaxies. The CNN reaches an accuracy ~98% on our testing datasets. We apply this CNN to galaxy catalogs from three amongst the largest surveys available today: the Sloan Digital Sky Survey (SDSS), the DESI Legacy Imaging Surveys and the Panoramic Survey Telescope and Rapid Response System Survey (Pan-STARSS). We find that, even when strict selection criteria are used, all catalogs still show a ~5% level of contamination from galaxy shreds. Our CNN gives a simple yet effective solution to clean galaxy catalogs from these contaminants.","url_abs":"https://arxiv.org/abs/2301.09648v1","url_pdf":"https://arxiv.org/pdf/2301.09648v1.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":"identification-of-galaxy-shreds-in-large","repo_url":"https://github.com/editeodoro/cnn_shreds","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}