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Artikel ; Online: Cosmic Ray Background Removal With Deep Neural Networks in SBND.

Acciarri, R / Adams, C / Andreopoulos, C / Asaadi, J / Babicz, M / Backhouse, C / Badgett, W / Bagby, L / Barker, D / Basque, V / Bazetto, M C Q / Betancourt, M / Bhanderi, A / Bhat, A / Bonifazi, C / Brailsford, D / Brandt, A G / Brooks, T / Carneiro, M F /
Chen, Y / Chen, H / Chisnall, G / Crespo-Anadón, J I / Cristaldo, E / Cuesta, C / de Icaza Astiz, I L / De Roeck, A / de Sá Pereira, G / Del Tutto, M / Di Benedetto, V / Ereditato, A / Evans, J J / Ezeribe, A C / Fitzpatrick, R S / Fleming, B T / Foreman, W / Franco, D / Furic, I / Furmanski, A P / Gao, S / Garcia-Gamez, D / Frandini, H / Ge, G / Gil-Botella, I / Gollapinni, S / Goodwin, O / Green, P / Griffith, W C / Guenette, R / Guzowski, P / Ham, T / Henzerling, J / Holin, A / Howard, B / Jones, R S / Kalra, D / Karagiorgi, G / Kashur, L / Ketchum, W / Kim, M J / Kudryavtsev, V A / Larkin, J / Lay, H / Lepetic, I / Littlejohn, B R / Louis, W C / Machado, A A / Malek, M / Mardsen, D / Mariani, C / Marinho, F / Mastbaum, A / Mavrokoridis, K / McConkey, N / Meddage, V / Méndez, D P / Mettler, T / Mistry, K / Mogan, A / Molina, J / Mooney, M / Mora, L / Moura, C A / Mousseau, J / Navrer-Agasson, A / Nicolas-Arnaldos, F J / Nowak, J A / Palamara, O / Pandey, V / Pater, J / Paulucci, L / Pimentel, V L / Psihas, F / Putnam, G / Qian, X / Raguzin, E / Ray, H / Reggiani-Guzzo, M / Rivera, D / Roda, M / Ross-Lonergan, M / Scanavini, G / Scarff, A / Schmitz, D W / Schukraft, A / Segreto, E / Soares Nunes, M / Soderberg, M / Söldner-Rembold, S / Spitz, J / Spooner, N J C / Stancari, M / Stenico, G V / Szelc, A / Tang, W / Tena Vidal, J / Torretta, D / Toups, M / Touramanis, C / Tripathi, M / Tufanli, S / Tyley, E / Valdiviesso, G A / Worcester, E / Worcester, M / Yarbrough, G / Yu, J / Zamorano, B / Zennamo, J / Zglam, A

Frontiers in artificial intelligence

2021  Band 4, Seite(n) 649917

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 ... ...

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.
Sprache Englisch
Erscheinungsdatum 2021-08-24
Erscheinungsland Switzerland
Dokumenttyp Journal Article
ISSN 2624-8212
ISSN (online) 2624-8212
DOI 10.3389/frai.2021.649917
Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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