Cosmic Ray Background Removal With Deep Neural Networks in SBND
| Autor: | J. Asaadi, V. Basque, M. Tripathi, G. Yarbrough, E. Raguzin, G. Scanavini, D. Kalra, Carla Bonifazi, N. McConkey, V. Meddage, A. M. Szelc, H. Ray, I. K. Furic, A. Bhanderi, I. Lepetic, W. C. Louis, M. Worcester, G. Karagiorgi, Vishvas Pandey, J. Spitz, M. Ross-Lonergan, W. Ketchum, L. Mora, T. Ham, B. Zamorano, T. Mettler, Andrew Brandt, O. Goodwin, G. Ge, C. A. Moura, V. A. Kudryavtsev, M. C. Q. Bazetto, A. Holin, Corey Adams, J. A. Nowak, D. P. Méndez, M. Roda, William Tang, M. F. Carneiro, J. I. Crespo-Anadón, L. Bagby, C. Andreopoulos, C. Cuesta, A. Navrer-Agasson, V. Di Benedetto, D. Franco, S. Tufanli, A. Scarff, B.T. Fleming, G. V. Stenico, D. Torretta, Xiaohui Qian, M. Toups, D. W. Schmitz, R. S. Fitzpatrick, H. Frandini, W. C. Griffith, S. Söldner-Rembold, B. Howard, J. Tena Vidal, E. Cristaldo, Ornella Palamara, A. P. Furmanski, S. Gollapinni, John Evans, A. A. Machado, A. Bhat, F. Psihas, R. Acciarri, Jorge Molina, N. J. C. Spooner, V. L. Pimentel, J. Zennamo, A. De Roeck, D. Barker, Roland S.G. Jones, K. Mistry, Joleen Pater, A. C. Ezeribe, Yi Chen, F. Nicolas-Arnaldos, M. Reggiani-Guzzo, M. Soderberg, T. Brooks, A. Schukraft, W. Badgett, D. Mardsen, G. A. Valdiviesso, G. Putnam, Patrick Green, A. Zglam, F. Marinho, I. L. De Icaza Astiz, R. Guenette, D. Brailsford, C. Backhouse, Laura Paulucci, M. Betancourt, A. Mastbaum, J. Mousseau, D. Garcia-Gamez, Jong-Sung Yu, M. Del Tutto, H. S. Chen, K. Mavrokoridis, S. Gao, C. Touramanis, Antonio Ereditato, J. Larkin, M. Soares Nunes, A. Mogan, I. Gil-Botella, M. Stancari, L. Kashur, E. Segreto, E. T. Worcester, M. J. Kim, G. Chisnall, W. Foreman, J. Henzerling, H. Lay, M. Babicz, P. Guzowski, G. de Sá Pereira, M. Mooney, E. Tyley, D. Rivera, C. Mariani, M. Malek, B. R. Littlejohn |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Liquid Ar detectors
SBN program Physics::Instrumentation and Detectors Astrophysics::High Energy Astrophysical Phenomena Other Fields of Physics FOS: Physical sciences Cosmic ray 01 natural sciences physics.data-an Nuclear physics Artificial Intelligence neutrino physics 0103 physical sciences Fermilab 010306 general physics Projection (set theory) liquid Ar detectors Original Research Physics Multidisciplinary COSMIC cancer database 010308 nuclear & particles physics Detector deep learning UNet Deep learning QA75.5-76.95 Neutrino physics Physics - Data Analysis Statistics and Probability Electronic computers. Computer science SBND Particle High Energy Physics::Experiment Neutrino Event (particle physics) Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | Frontiers in Artificial Intelligence Frontiers in Artificial Intelligence, Vol 4 (2021) Digibug. Repositorio Institucional de la Universidad de Granada instname Frontiers in artificial intelligence |
| ISSN: | 2624-8212 |
| Popis: | 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 DEAC02- 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. 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 fromsurface 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. U.S. Department of Energy, Office of Science, Office of High Energy Physics U.S. National Science Foundation Science and Technology Facilities Council (STFC) The Royal Society of the United Kingdom Swiss National Science Foundation Spanish Ministerio de Ciencia e Innovación (PID2019-104676GB-C32) Junta de Andalucía (SOMM17/6104/UGR, P18-FR-4314) FEDER Funds São Paulo Research Foundation (FAPESP) National Council of Scientific and Technological Development (CNPq) of Brazil Los Alamos National Laboratory for LDRD Argonne Leadership Computing Facility Fermi National Accelerator Laboratory (Fermilab) Fermi Research Alliance, LLC (FRA) DE-AC02-07CH11359 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|