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Start Over Author "Bazetto MCQ" Topic 51 physical sciences
2 results on '"Bazetto MCQ"'

1. Highly-parallelized simulation of a pixelated LArTPC on a GPU

Author

Abed Abud, A, Abi, B, Acciarri, R, Acero, MA, Adames, MR, Adamov, G, Adamowski, M, Adams, D, Adinolfi, M, Adriano, C, Aduszkiewicz, A, Aguilar, J, Ahmad, Z, Ahmed, J, Aimard, B, Akbar, F, Allison, K, Alonso Monsalve, S, Alrashed, M, Alt, C, Alton, A, Alvarez, R, Amedo, P, Anderson, J, Andrade, DA, Andreopoulos, C, Andreotti, M, Andrews, MP, Andrianala, F, Andringa, S, Anfimov, N, Anicézio Campanelli, WL, Ankowski, A, Antoniassi, M, Antonova, M, Antoshkin, A, Antusch, S, Aranda-Fernandez, A, Arellano, L, Arnold, LO, Arroyave, MA, Asaadi, J, Ashkenazi, A, Asquith, L, Aurisano, A, Aushev, V, Autiero, D, Ayala-Torres, M, Azfar, F, Back, A, Back, H, Back, JJ, Bagaturia, I, Bagby, L, Balashov, N, Balasubramanian, S, Baldi, P, Baldini, W, Baller, B, Bambah, B, Barao, F, Barenboim, G, Barham Alzás, P, Barker, GJ, Barkhouse, W, Barnes, C, Barr, G, Barranco Monarca, J, Barros, A, Barros, N, Barrow, JL, Basharina-Freshville, A, Bashyal, A, Basque, V, Batchelor, C, Battat, JBR, Battisti, F, Bay, F, Bazetto, MCQ, Bazo Alba, JLL, Beacom, JF, Bechetoille, E, Behera, B, Belchior, E, Bellantoni, L, Bellettini, G, Bellini, V, Beltramello, O, Benekos, N, Benitez Montiel, C, Benjamin, D, Bento Neves, F, Berger, J, Berkman, S, Bernardini, P, Berner, RM, Bersani, A, Bertolucci, S, Betancourt, M, Betancur Rodríguez, A, and Apollo - University of Cambridge Repository

Subjects
Bioengineering, 5106 Nuclear and Plasma Physics, 51 Physical Sciences
Abstract

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

Published
2023

2. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Author

Abed Abud, A, Abi, B, Acciarri, R, Acero, MA, Adames, MR, Adamov, G, Adamowski, M, Adams, D, Adinolfi, M, Aduszkiewicz, A, Aguilar, J, Ahmad, Z, Ahmed, J, Aimard, B, Ali-Mohammadzadeh, B, Alion, T, Allison, K, Alonso Monsalve, S, AlRashed, M, Alt, C, Alton, A, Alvarez, R, Amedo, P, Anderson, J, Andreopoulos, C, Andreotti, M, Andrews, M, Andrianala, F, Andringa, S, Anfimov, N, Ankowski, A, Antoniassi, M, Antonova, M, Antoshkin, A, Antusch, S, Aranda-Fernandez, A, Arellano, L, Arnold, LO, Arroyave, MA, Asaadi, J, Asquith, L, Aurisano, A, Aushev, V, Autiero, D, Ayala Lara, V, Ayala-Torres, M, Azfar, F, Babicz, M, Back, A, Back, H, Back, JJ, Backhouse, C, Bagaturia, I, Bagby, L, Balashov, N, Balasubramanian, S, Baldi, P, Baller, B, Bambah, B, Barao, F, Barenboim, G, Barker, G, Barkhouse, W, Barnes, C, Barr, G, Barranco Monarca, J, Barros, A, Barros, N, Barrow, JL, Basharina-Freshville, A, Bashyal, A, Basque, V, Batchelor, C, Batista Das Chagas, E, Battat, J, Battisti, F, Bay, F, Bazetto, MCQ, Bazo Alba, J, Beacom, JF, Bechetoille, E, Behera, B, Beigbeder, C, Bellantoni, L, Bellettini, G, Bellini, V, Beltramello, O, Benekos, N, Benitez Montiel, C, Bento Neves, F, Berger, J, Berkman, S, Bernardini, P, Berner, RM, Bersani, A, Bertolucci, S, Betancourt, M, Betancur Rodríguez, A, Bevan, A, Bezawada, Y, Yang, T. [0000-0002-3190-9941], Apollo - University of Cambridge Repository, and Yang, T [0000-0002-3190-9941]

Subjects
Regular Article - Experimental Physics, 7 Affordable and Clean Energy, 5106 Nuclear and Plasma Physics, 51 Physical Sciences, 5107 Particle and High Energy Physics
Abstract

Funder: SERI and SNSF, Switzerland, Funder: Instituto Nazionale di Fisica Nucleare; doi: http://dx.doi.org/10.13039/501100004007, Funder: CAM, Fundacion “La Caixa”, Junta de Andalucia-FEDER, MICINN, and Xunta de Galicia, Spain, Funder: CERN; doi: http://dx.doi.org/10.13039/100012470, Funder: DOE and NSF, United States of America, Funder: CFI, IPP and NSERC, Canada, Funder: CNRS/IN2P3 and CEA, France, Funder: The Royal Society and UKRI/STFC, United Kingdom, Funder: ERDF, H2020-EU and MSCA, European Union, Funder: NRF, South Korea, Funder: FCT, Portugal, Funder: MSMT, Czech Republic, Funder: CNPq, FAPERJ, FAPEG and FAPESP, Brazil, Funder: TUB.ITAK, Turkey, Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.

Published
2022

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