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2 results on '"Bazetto, MCQ"'

1. Cosmic ray background removal with deep neural networks in SBND

Author

Collaboration, SBND, Acciarri, R, Adams, C, Andreopoulos, C, Asaadi, J, Babicz, M, Backhouse, C, Badgett, W, Bagby, L, Barker, D, Basque, V, Bazetto, MCQ, Betancourt, M, Bhanderi, A, Bhat, A, Bonifazi, C, Brailsford, D, Brandt, AG, Brooks, T, Carneiro, MF, Chen, Y, Chen, H, Chisnall, G, Crespo-Anadón, JI, Cristaldo, E, Cuesta, C, Astiz, ILDI, Roeck, AD, Pereira, GDS, Tutto, MD, Benedetto, VD, Ereditato, A, Evans, JJ, Ezeribe, AC, Fitzpatrick, RS, Fleming, BT, Foreman, W, Franco, D, Furic, I, Furmanski, AP, Gao, S, Garcia-Gamez, D, Frandini, H, Ge, G, Gil-Botella, I, Gollapinni, S, Goodwin, O, Green, P, Griffith, WC, Guenette, R, Guzowski, P, Ham, T, Henzerling, J, Holin, A, Howard, B, Jones, RS, Kalra, D, Karagiorgi, G, Kashur, L, Ketchum, W, Kim, MJ, Kudryavtsev, VA, Larkin, J, Lay, H, Lepetic, I, Littlejohn, BR, Louis, WC, Machado, AA, Malek, M, Mardsen, D, Mariani, C, Marinho, F, Mastbaum, A, Mavrokoridis, K, McConkey, N, Meddage, V, Méndez, DP, Mettler, T, Mistry, K, Mogan, A, Molina, J, Mooney, M, Mora, L, Moura, CA, Mousseau, J, Navrer-Agasson, A, Nicolas-Arnaldos, FJ, Nowak, JA, Palamara, O, Pandey, V, Pater, J, Paulucci, L, Pimentel, VL, 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, DW, Schukraft, A, Segreto, E, Nunes, MS, Soderberg, M, Söldner-Rembold, S, Spitz, J, Spooner, NJC, Stancari, M, Stenico, GV, Szelc, A, Tang, W, Vidal, JT, Torretta, D, Toups, M, Touramanis, C, Tripathi, M, Tufanli, S, Tyley, E, Valdiviesso, GA, Worcester, E, Worcester, M, Yarbrough, G, Yu, J, Zamorano, B, Zennamo, J, and Zglam, A

Subjects
Physics::Instrumentation and Detectors, Astrophysics::High Energy Astrophysical Phenomena, High Energy Physics::Experiment
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.

Published
2021

2. Cosmic Ray Background Removal With Deep Neural Networks in SBND.

Author

Acciarri R, Adams C, Andreopoulos C, Asaadi J, Babicz M, Backhouse C, Badgett W, Bagby L, Barker D, Basque V, Bazetto MCQ, Betancourt M, Bhanderi A, Bhat A, Bonifazi C, Brailsford D, Brandt AG, Brooks T, Carneiro MF, Chen Y, Chen H, Chisnall G, Crespo-Anadón JI, Cristaldo E, Cuesta C, de Icaza Astiz IL, De Roeck A, de Sá Pereira G, Del Tutto M, Di Benedetto V, Ereditato A, Evans JJ, Ezeribe AC, Fitzpatrick RS, Fleming BT, Foreman W, Franco D, Furic I, Furmanski AP, Gao S, Garcia-Gamez D, Frandini H, Ge G, Gil-Botella I, Gollapinni S, Goodwin O, Green P, Griffith WC, Guenette R, Guzowski P, Ham T, Henzerling J, Holin A, Howard B, Jones RS, Kalra D, Karagiorgi G, Kashur L, Ketchum W, Kim MJ, Kudryavtsev VA, Larkin J, Lay H, Lepetic I, Littlejohn BR, Louis WC, Machado AA, Malek M, Mardsen D, Mariani C, Marinho F, Mastbaum A, Mavrokoridis K, McConkey N, Meddage V, Méndez DP, Mettler T, Mistry K, Mogan A, Molina J, Mooney M, Mora L, Moura CA, Mousseau J, Navrer-Agasson A, Nicolas-Arnaldos FJ, Nowak JA, Palamara O, Pandey V, Pater J, Paulucci L, Pimentel VL, 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 DW, Schukraft A, Segreto E, Soares Nunes M, Soderberg M, Söldner-Rembold S, Spitz J, Spooner NJC, Stancari M, Stenico GV, Szelc A, Tang W, Tena Vidal J, Torretta D, Toups M, Touramanis C, Tripathi M, Tufanli S, Tyley E, Valdiviesso GA, Worcester E, Worcester M, Yarbrough G, Yu J, Zamorano B, Zennamo J, and Zglam A

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Acciarri, Adams, Andreopoulos, Asaadi, Babicz, Backhouse, Badgett, Bagby, Barker, Basque, Bazetto, Betancourt, Bhanderi, Bhat, Bonifazi, Brailsford, Brandt, Brooks, Carneiro, Chen, Chen, Chisnall, Crespo-Anadón, Cristaldo, Cuesta, de Icaza Astiz, De Roeck, de Sá Pereira, Del Tutto, Di Benedetto, Ereditato, Evans, Ezeribe, Fitzpatrick, Fleming, Foreman, Franco, Furic, Furmanski, Gao, Garcia-Gamez, Frandini, Ge, Gil-Botella, Gollapinni, Goodwin, Green, Griffith, Guenette, Guzowski, Ham, Henzerling, Holin, Howard, Jones, Kalra, Karagiorgi, Kashur, Ketchum, Kim, Kudryavtsev, Larkin, Lay, Lepetic, Littlejohn, Louis, Machado, Malek, Mardsen, Mariani, Marinho, Mastbaum, Mavrokoridis, McConkey, Meddage, Méndez, Mettler, Mistry, Mogan, Molina, Mooney, Mora, Moura, Mousseau, Navrer-Agasson, Nicolas-Arnaldos, Nowak, Palamara, Pandey, Pater, Paulucci, Pimentel, Psihas, Putnam, Qian, Raguzin, Ray, Reggiani-Guzzo, Rivera, Roda, Ross-Lonergan, Scanavini, Scarff, Schmitz, Schukraft, Segreto, Soares Nunes, Soderberg, Söldner-Rembold, Spitz, Spooner, Stancari, Stenico, Szelc, Tang, Tena Vidal, Torretta, Toups, Touramanis, Tripathi, Tufanli, Tyley, Valdiviesso, Worcester, Worcester, Yarbrough, Yu, Zamorano, Zennamo and Zglam.)

Published
2021
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