{"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/cosmic-background-removal-with-deep-neural","title":"Cosmic Background Removal with Deep Neural Networks in SBND","arxiv_id":"2012.01301","date":"2020-12-02","proceeding":null,"authors":["SBND Collaboration","R. Acciarri","C. Adams","C. Andreopoulos","J. Asaadi","M. Babicz","C. Backhouse","W. Badgett","L. Bagby","D. Barker","V. Basque","M. C. Q. Bazetto","M. Betancourt","A. Bhanderi","A. Bhat","C. Bonifazi","D. Brailsford","A. G. Brandt","T. Brooks","M. F. Carneiro","Y. Chen","H. Chen","G. Chisnall","J. I. Crespo-Anadón","E. Cristaldo","C. Cuesta","I. L. de Icaza Astiz","A. De Roeck","G. de Sá Pereira","M. Del Tutto","V. Di Benedetto","A. Ereditato","J. J. Evans","A. C. Ezeribe","R. S. Fitzpatrick","B. T. Fleming","W. Foreman","D. Franco","I. Furic","A. P. Furmanski","S. Gao","D. Garcia-Gamez","H. Frandini","G. Ge","I. Gil-Botella","S. Gollapinni","O. Goodwin","P. Green","W. C. Griffith","R. Guenette","P. Guzowski","T. Ham","J. Henzerling","A. Holin","B. Howard","R. S. Jones","D. Kalra","G. Karagiorgi","L. Kashur","W. Ketchum","M. J. Kim","V. A. Kudryavtsev","J. Larkin","H. Lay","I. Lepetic","B. R. Littlejohn","W. C. Louis","A. A. Machado","M. Malek","D. Mardsen","C. Mariani","F. Marinho","A. Mastbaum","K. Mavrokoridis","N. McConkey","V. Meddage","D. P. Méndez","T. Mettler","K. Mistry","A. Mogan","J. Molina","M. Mooney","L. Mora","C. A. Moura","J. Mousseau","A. Navrer-Agasson","F. J. Nicolas-Arnaldos","J. A. Nowak","O. Palamara","V. Pandey","J. Pater","L. Paulucci","V. L. Pimentel","F. Psihas","G. Putnam","X. Qian","E. Raguzin","H. Ray","M. Reggiani-Guzzo","D. Rivera","M. Roda","M. Ross-Lonergan","G. Scanavini","A. Scarff","D. W. Schmitz","A. Schukraft","E. Segreto","M. Soares Nunes","M. Soderberg","S. Söldner-Rembold","J. Spitz","N. J. C. Spooner","M. Stancari","G. V. Stenico","A. Szelc","W. Tang","J. Tena Vidal","D. Torretta","M. Toups","C. Touramanis","M. Tripathi","S. Tufanli","E. Tyley","G. A. Valdiviesso","E. Worcester","M. Worcester","G. Yarbrough","J. Yu","B. Zamorano","J. Zennamo","A. Zglam"],"abstract":"In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.","url_abs":"https://arxiv.org/abs/2012.01301v3","url_pdf":"https://arxiv.org/pdf/2012.01301v3.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":"cosmic-background-removal-with-deep-neural","repo_url":"https://github.com/coreyjadams/CosmicTagger","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","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}