Cosmic Ray Background Removal With Deep Neural Networks in SBND

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Acciarri, R, Adams, C, Andreopoulos, C ORCID: 0000-0003-2020-8215, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D, Basque, V et al (show 120 more authors) , Bazetto, MCQ, Betancourt, M, Bhanderi, A, Bhat, A, Bonifazi, C, Brailsford, D, Brandt, AG, Brooks, T, Carneiro, MF, Chen, Y, Chen, H, Chisnall, G, Crespo-Anadon, JI, Cristaldo, E, Cuesta, C, Astiz, IL de Icaza, De Roeck, A, Pereira, G de Sa, Del Tutto, M, Di Benedetto, V, Ereditato, A, Evans, JJ, Ezeribe, AC, Fitzpatrick, RS, Fleming, BT, Foreman, W, Franco, D, Furic, I, Furmanski, AP, Gao, S, Garcia-Gamez, D, Frandini, H, Ge, G, Gil-Botella, I, Gollapinni, S, Goodwin, O, Green, P, Griffith, WC, Guenette, R, Guzowski, P, Ham, T, Henzerling, J, Holin, A, Howard, B, Jones, RS, Kalra, D, Karagiorgi, G, Kashur, L, Ketchum, W, Kim, MJ, Kudryavtsev, VA, Larkin, J, Lay, H, Lepetic, I, Littlejohn, BR, Louis, WC, Machado, AA, Malek, M, Mardsen, D, Mariani, C, Marinho, F, Mastbaum, A, Mavrokoridis, K ORCID: 0000-0002-9244-4519, McConkey, N, Meddage, V, Mendez, DP, Mettler, T, Mistry, K, Mogan, A, Molina, J, Mooney, M, Mora, L, Moura, CA, Mousseau, J, Navrer-Agasson, A, Nicolas-Arnaldos, FJ, Nowak, JA, Palamara, O, Pandey, V, Pater, J, Paulucci, L, Pimentel, VL, Psihas, F, Putnam, G, Qian, X, Raguzin, E, Ray, H, Reggiani-Guzzo, M, Rivera, D, Roda, M ORCID: 0000-0002-1569-4397, Ross-Lonergan, M, Scanavini, G, Scarff, A, Schmitz, DW, Schukraft, A, Segreto, E, Nunes, M Soares, Soderberg, M, Soldner-Rembold, S, Spitz, J, Spooner, NJC, Stancari, M, Stenico, GV, Szelc, A, Tang, W, Vidal, J Tena, Torretta, D, Toups, M, Touramanis, C ORCID: 0000-0001-5191-2171, Tripathi, M, Tufanli, S, Tyley, E, Valdiviesso, GA, Worcester, E, Worcester, M, Yarbrough, G, Yu, J, Zamorano, B, Zennamo, J and Zglam, A (2021) Cosmic Ray Background Removal With Deep Neural Networks in SBND. FRONTIERS IN ARTIFICIAL INTELLIGENCE, 4. 649917-.

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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 deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Item Type:	Article
Uncontrolled Keywords:	deep learning, neutrino physics, SBN program, SBND, UNet, liquid Ar detectors
Depositing User:	Symplectic Admin
Date Deposited:	13 Jan 2021 09:58
Last Modified:	18 Jan 2023 23:03
DOI:	10.3389/frai.2021.649917
Open Access URL:	https://www.frontiersin.org/articles/10.3389/frai....
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3113381