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.
| Original language | English (US) |
|---|---|
| Article number | 649917 |
| Journal | Frontiers in Artificial Intelligence |
| Volume | 4 |
| DOIs | |
| State | Published - Aug 24 2021 |
Keywords
- deep learning
- liquid Ar detectors
- neutrino physics
- SBN program
- SBND
- UNet
ASJC Scopus subject areas
- Artificial Intelligence
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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.
In: Frontiers in Artificial Intelligence, Vol. 4, 649917, 24.08.2021.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Cosmic Ray Background Removal With Deep Neural Networks in SBND
AU - Acciarri, R.
AU - Adams, C.
AU - Andreopoulos, C.
AU - Asaadi, J.
AU - Babicz, M.
AU - Backhouse, C.
AU - Badgett, W.
AU - Bagby, L.
AU - Barker, D.
AU - Basque, V.
AU - Bazetto, M. C.Q.
AU - Betancourt, M.
AU - Bhanderi, A.
AU - Bhat, A.
AU - Bonifazi, C.
AU - Brailsford, D.
AU - Brandt, A. G.
AU - Brooks, T.
AU - Carneiro, M. F.
AU - Chen, Y.
AU - Chen, H.
AU - Chisnall, G.
AU - Crespo-Anadón, J. I.
AU - Cristaldo, E.
AU - Cuesta, C.
AU - de Icaza Astiz, I. L.
AU - De Roeck, A.
AU - de Sá Pereira, G.
AU - Del Tutto, M.
AU - Di Benedetto, V.
AU - Ereditato, A.
AU - Evans, J. J.
AU - Ezeribe, A. C.
AU - Fitzpatrick, R. S.
AU - Fleming, B. T.
AU - Foreman, W.
AU - Franco, D.
AU - Furic, I.
AU - Furmanski, A. P.
AU - Gao, S.
AU - Garcia-Gamez, D.
AU - Frandini, H.
AU - Ge, G.
AU - Gil-Botella, I.
AU - Gollapinni, S.
AU - Goodwin, O.
AU - Green, P.
AU - Griffith, W. C.
AU - Guenette, R.
AU - Guzowski, P.
AU - Ham, T.
AU - Henzerling, J.
AU - Holin, A.
AU - Howard, B.
AU - Jones, R. S.
AU - Kalra, D.
AU - Karagiorgi, G.
AU - Kashur, L.
AU - Ketchum, W.
AU - Kim, M. J.
AU - Kudryavtsev, V. A.
AU - Larkin, J.
AU - Lay, H.
AU - Lepetic, I.
AU - Littlejohn, B. R.
AU - Louis, W. C.
AU - Machado, A. A.
AU - Malek, M.
AU - Mardsen, D.
AU - Mariani, C.
AU - Marinho, F.
AU - Mastbaum, A.
AU - Mavrokoridis, K.
AU - McConkey, N.
AU - Meddage, V.
AU - Méndez, D. P.
AU - Mettler, T.
AU - Mistry, K.
AU - Mogan, A.
AU - Molina, J.
AU - Mooney, M.
AU - Mora, L.
AU - Moura, C. A.
AU - Mousseau, J.
AU - Navrer-Agasson, A.
AU - Nicolas-Arnaldos, F. J.
AU - Nowak, J. A.
AU - Palamara, O.
AU - Pandey, V.
AU - Pater, J.
AU - Paulucci, L.
AU - Pimentel, V. L.
AU - Psihas, F.
AU - Putnam, G.
AU - Qian, X.
AU - Raguzin, E.
AU - Ray, H.
AU - Reggiani-Guzzo, M.
AU - Rivera, D.
AU - Roda, M.
AU - Ross-Lonergan, M.
AU - Scanavini, G.
AU - Scarff, A.
AU - Schmitz, D. W.
AU - Schukraft, A.
AU - Segreto, E.
AU - Soares Nunes, M.
AU - Soderberg, M.
AU - Söldner-Rembold, S.
AU - Spitz, J.
AU - Spooner, N. J.C.
AU - Stancari, M.
AU - Stenico, G. V.
AU - Szelc, A.
AU - Tang, W.
AU - Tena Vidal, J.
AU - Torretta, D.
AU - Toups, M.
AU - Touramanis, C.
AU - Tripathi, M.
AU - Tufanli, S.
AU - Tyley, E.
AU - Valdiviesso, G. A.
AU - Worcester, E.
AU - Worcester, M.
AU - Yarbrough, G.
AU - Yu, J.
AU - Zamorano, B.
AU - Zennamo, J.
AU - Zglam, A.
N1 - Funding Information: The SBND Collaboration acknowledges the generous support of the following organizations: the U.S. Department of Energy, Office of Science, Office of High Energy Physics; the U.S. National Science Foundation; the Science and Technology Facilities Council (STFC), part of United Kingdom Research and Innovation, and The Royal Society of the United Kingdom; the Swiss National Science Foundation; the Spanish Ministerio de Ciencia e Innovaci?n (PID2019-104676GB-C32) and Junta de Andaluc?a (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds; and the S?o Paulo Research Foundation (FAPESP) and the National Council of Scientific and Technological Development (CNPq) of Brazil. We acknowledge Los Alamos National Laboratory for LDRD funding. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. SBND is an experiment at the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. Funding Information: The SBND Collaboration acknowledges the generous support of the following organizations: the U.S. Department of Energy, Office of Science, Office of High Energy Physics; the U.S. National Science Foundation; the Science and Technology Facilities Council (STFC), part of United Kingdom Research and Innovation, and The Royal Society of the United Kingdom; the Swiss National Science Foundation; the Spanish Ministerio de Ciencia e Innovación (PID2019-104676GB-C32) and Junta de Andalucía (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds; and the São Paulo Research Foundation (FAPESP) and the National Council of Scientific and Technological Development (CNPq) of Brazil. We acknowledge Los Alamos National Laboratory for LDRD funding. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. SBND is an experiment at the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Publisher Copyright: © 2021 Acciarri, Adams, Andreopoulos, Asaadi.
PY - 2021/8/24
Y1 - 2021/8/24
N2 - 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.
AB - 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.
KW - deep learning
KW - liquid Ar detectors
KW - neutrino physics
KW - SBN program
KW - SBND
KW - UNet
UR - http://www.scopus.com/inward/record.url?scp=85118213576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118213576&partnerID=8YFLogxK
U2 - 10.3389/frai.2021.649917
DO - 10.3389/frai.2021.649917
M3 - Article
AN - SCOPUS:85118213576
VL - 4
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
SN - 2624-8212
M1 - 649917
ER -