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Article
Report number arXiv:2012.01301 ; FERMILAB-PUB-20-641-ND
Title Cosmic Ray Background Removal With Deep Neural Networks in SBND
Related titleCosmic Background Removal with Deep Neural Networks in SBND
Author(s)

Acciarri, R. (Fermilab) ; Adams, C. (Argonne) ; Andreopoulos, C. (Liverpool U. ; Rutherford) ; Asaadi, J. (Texas U., Arlington) ; Babicz, M. (CERN) ; Backhouse, C. (University Coll. London) ; Badgett, W. (Fermilab) ; Bagby, L. (Fermilab) ; Barker, D. (Sheffield U.) ; Basque, V. (Manchester U.) ; Bazetto, M.C. Q. (Campinas State U.) ; Betancourt, M. (Fermilab) ; Bhanderi, A. (Manchester U.) ; Bhat, A. (Syracuse U.) ; Bonifazi, C. (UFRJ, Rio de Janeiro) ; Brailsford, D. (Lancaster U.) ; Brandt, A.G. (Texas U., Arlington) ; Brooks, T. (Sheffield U.) ; Carneiro, M.F. (Brookhaven Natl. Lab.) ; Chen, Y. (Bern U.) ; Chen, H. (Brookhaven Natl. Lab.) ; Chisnall, G. (Sussex U.) ; Crespo-Anadón, J.I. (Madrid, CIEMAT) ; Cristaldo, E. (Asuncion Natl. U.) ; Cuesta, C. (Madrid, CIEMAT) ; de Icaza Astiz, I.L. (Sussex U.) ; De Roeck, A. (CERN) ; de Sa Pereira, G. (Liverpool U. ; Rutherford) ; Del Tutto, M. (Fermilab) ; Di Benedetto, V. (Fermilab) ; Ereditato, A. (Bern U.) ; Evans, J.J. (Manchester U.) ; Ezeribe, A.C. (Sheffield U.) ; Fitzpatrick, R.S. (Michigan U.) ; Fleming, B.T. (Yale U.) ; Foreman, W. (IIT, Chicago) ; Franco, D. (Yale U.) ; Furic, I. (Florida U.) ; Furmanski, A.P. (Minnesota U.) ; Gao, S. (Brookhaven Natl. Lab.) ; Garcia-Gamez, D. (CAFPE, Granada) ; Frandini, H. (Campinas State U.) ; Ge, G. (Columbia U.) ; Gil-Botella, I. (Madrid, CIEMAT) ; Gollapinni, S. (Los Alamos) ; Goodwin, O. (Manchester U.) ; Green, P. (Manchester U.) ; Griffith, W.C. (Sussex U.) ; Guenette, R. (Harvard U.) ; Guzowski, P. (Manchester U.) ; Ham, T. (Liverpool U.) ; Henzerling, J. (Liverpool U.) ; Holin, A. (University Coll. London) ; Howard, B. (Fermilab) ; Jones, R. S. (Liverpool U.) ; Kalra, D. (Columbia U.) ; Karagiorgi, G. (Columbia U.) ; Kashur, L. (Colorado State U.) ; Ketchum, W. (Fermilab) ; Kim, M.J. (Fermilab) ; Kudryavtsev, V.A. (Sheffield U.) ; Larkin, J. (Brookhaven Natl. Lab.) ; Lay, H. (Lancaster U.) ; Lepetic, I. (Rutgers U., Piscataway) ; Littlejohn, B.R. (IIT, Chicago) ; Louis, W.C. (Los Alamos) ; Machado, A. A. (U. Campinas) ; Malek, M. (Sheffield U.) ; Mardsen, D. (Manchester U.) ; Mariani, C. (Virginia Tech.) ; Marinho, F. (Sao Carlos Federal U.) ; Mastbaum, A. (Rutgers U., Piscataway) ; Mavrokoridis, K. (Liverpool U.) ; McConkey, N. (Manchester U.) ; Meddage, V. (Florida U.) ; Méndez, D.P. (Brookhaven Natl. Lab.) ; Mettler, T. (Bern U.) ; Mistry, K. (Manchester U.) ; Mogan, A. (U. Tennessee, Knoxville) ; Molina, J. (Asuncion Natl. U.) ; Mooney, M. (Colorado State U.) ; Mora, L. (Manchester U.) ; Moura, C.A. (ABC Federal U.) ; Mousseau, J. (U. Michigan, Ann Arbor) ; Navrer-Agasson, A. (Manchester U.) ; Nicolas-Arnaldos, F. J. (Granada U.) ; Nowak, J.A. (Lancaster U.) ; Palamara, O. (Fermilab) ; Pandey, V. (Florida U.) ; Pater, J. (Manchester U.) ; Paulucci, L. (ABC Federal U.) ; Pimentel, V.L. (Campinas State U.) ; Psihas, F. (Fermilab) ; Putnam, G. (Chicago U., EFI) ; Qian, X. (Brookhaven Natl. Lab.) ; Raguzin, E. (Brookhaven Natl. Lab.) ; Ray, H. (Florida U.) ; Reggiani-Guzzo, M. (Manchester U.) ; Rivera, D. (Pennsylvania U.) ; Roda, M. (Liverpool U.) ; Ross-Lonergan, M. (Columbia U.) ; Scanavini, G. (Yale U.) ; Scarff, A. (Sheffield U.) ; Schmitz, D.W. (Chicago U., EFI) ; Schukraft, A. (Fermilab) ; Segreto, E. (U. Campinas) ; Soares Nunes, M. (Syracuse U.) ; Soderberg, M. (Syracuse U.) ; Söldner-Rembold, S. (Manchester U.) ; Spitz, J. (Michigan U.) ; Spooner, N.J. C. (Sheffield U.) ; Stancari, M. (Fermilab) ; Stenico, G.V. (Campinas State U.) ; Szelc, A. (Manchester U.) ; Tang, W. (Tennessee U.) ; Tena Vidal, J. (Liverpool U.) ; Torretta, D. (Fermilab) ; Toups, M. (Fermilab) ; Touramanis, C. (Liverpool U.) ; Tripathi, M. (Florida U.) ; Tufanli, S. (CERN) ; Tyley, E. (Sheffield U.) ; Valdiviesso, G.A. (Alfenas Fed. U., Pocos de Caldas) ; Worcester, E. (Brookhaven Natl. Lab.) ; Worcester, M. (Brookhaven Natl. Lab.) ; Yarbrough, G. (U. Tennessee, Knoxville) ; Yu, J. (Texas U., Arlington) ; Zamorano, B. (Granada U.) ; Zennamo, J. (Fermilab) ; Zglam, A. (Sheffield U.)

Publication 2021-08-24
Imprint 2020-12-02
Number of pages 17
In: Front. Artif. Intell. 4 (2021) 649917
DOI 10.3389/frai.2021.649917
Subject category physics.data-an ; Other Fields of Physics
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.
Copyright/License preprint: (License: arXiv nonexclusive-distrib 1.0)
publication: © 2021-2024 The authors (License: CC-BY-4.0)



Corresponding record in: Inspire


 Záznam vytvorený 2020-12-04, zmenený 2024-11-09


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