| Autor: |
R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, M. C. Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, A. G. Brandt, T. Brooks, M. F. Carneiro, Y. Chen, H. Chen, G. Chisnall, J. I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I. L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. J. Evans, A. C. Ezeribe, R. S. Fitzpatrick, B. T. Fleming, W. Foreman, D. Franco, I. Furic, A. P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, W. C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R. S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M. J. Kim, V. A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B. R. Littlejohn, W. C. Louis, A. A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, D. P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C. A. Moura, J. Mousseau, A. Navrer-Agasson, F. J. Nicolas-Arnaldos, J. A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V. L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D. W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N. J. C. Spooner, M. Stancari, G. V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G. A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Frontiers in Artificial Intelligence, Vol 4 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2624-8212 |
| DOI: |
10.3389/frai.2021.649917 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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