Accès ouvert déclaré
2020
preprint
Cosmic Background Removal with Deep Neural Networks in SBND
SBND Collaboration, 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. 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. Pereira, M. Del Tutto, Valerio 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. García-Gámez, H. Frandini, G. Ge, I. Gil‐Botella, S. Gollapinni, O. Goodwin, P. Green, W. C. Griffith, R. Guénette, P. Guzowski, Tae Jun Ham, J. Henzerling, A. Holin, B. Howard, R. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M. Kim, V. A. Kudryavtsev, Jeff 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, Jorge Molina, M. Mooney, L. de Mora, C. A. Moura, J. Mousseau, A. Navrer-Agasson, F. Nicolas-Arnaldos, J. Nowak, O. Palamara, V. Pandey, J. R. 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. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Söderberg, S. Söldner‐Rembold, J. Spitz, N. J. C. Spooner, M. Stancari, Gabriela Vitti Stenico, A. M. Szelc, W. Tang, Júlia Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G. A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. S. Yu, B. Zamorano, J. Zennamo, A. Zglam
7Citations signalées — pas une note de qualité
32Institutions déclarées
7Pays d’affiliation déclarés
Résumé fourni par la source
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.
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Sujets associés
Neutrino Physics ResearchParticle physics theoretical and experimental studiesAstrophysics and Cosmic Phenomena