Acciarri, R;
Adams, C;
Andreopoulos, C;
Asaadi, J;
Babicz, M;
Backhouse, C;
Badgett, W;
... Zglam, A; + view all
(2021)
Cosmic Ray Background Removal With Deep Neural Networks in SBND.
Frontiers in Artificial Intelligence
, 4
, Article 649917. 10.3389/frai.2021.649917.
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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.
| Type: | Article |
|---|---|
| Title: | Cosmic Ray Background Removal With Deep Neural Networks in SBND |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/frai.2021.649917 |
| Publisher version: | https://doi.org/10.3389/frai.2021.649917 |
| Language: | English |
| Additional information: | Copyright © 2021 Acciarri, Adams, Andreopoulos, Asaadi, Babicz, Backhouse, Badgett, Bagby, Barker, Basque, Bazetto, Betancourt, Bhanderi, Bhat, Bonifazi, Brailsford, Brandt, Brooks, Carneiro, Chen, Chen, Chisnall, Crespo-Anadón, Cristaldo, Cuesta, de Icaza Astiz, De Roeck, de Sá Pereira, Del Tutto, Di Benedetto, Ereditato, Evans, Ezeribe, Fitzpatrick, Fleming, Foreman, Franco, Furic, Furmanski, Gao, Garcia-Gamez, Frandini, Ge, Gil-Botella, Gollapinni, Goodwin, Green, Griffith, Guenette, Guzowski, Ham, Henzerling, Holin, Howard, Jones, Kalra, Karagiorgi, Kashur, Ketchum, Kim, Kudryavtsev, Larkin, Lay, Lepetic, Littlejohn, Louis, Machado, Malek, Mardsen, Mariani, Marinho, Mastbaum, Mavrokoridis, McConkey, Meddage, Méndez, Mettler, Mistry, Mogan, Molina, Mooney, Mora, Moura, Mousseau, Navrer-Agasson, Nicolas-Arnaldos, Nowak, Palamara, Pandey, Pater, Paulucci, Pimentel, Psihas, Putnam, Qian, Raguzin, Ray, Reggiani-Guzzo, Rivera, Roda, Ross-Lonergan, Scanavini, Scarff, Schmitz, Schukraft, Segreto, Soares Nunes, Soderberg, Söldner-Rembold, Spitz, Spooner, Stancari, Stenico, Szelc, Tang, Tena Vidal, Torretta, Toups, Touramanis, Tripathi, Tufanli, Tyley, Valdiviesso, Worcester, Worcester, Yarbrough, Yu, Zamorano, Zennamo and Zglam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | deep learning, neutrino physics, SBN program, SBND, UNet, liquid Ar detectors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10116762 |
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