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Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
dc.contributor.author | Abratenko, P. | |
dc.contributor.author | García Gámez, Diego | |
dc.contributor.author | MicroBooNE Collaboration | |
dc.date.accessioned | 2021-06-21T10:41:11Z | |
dc.date.available | 2021-06-21T10:41:11Z | |
dc.date.issued | 2021-05-14 | |
dc.identifier.citation | Abratenko, P... [et al.] (2021). Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber. Physical Review D, 103(9), 092003. DOI: [10.1103/PhysRevD.103.092003] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/69315 | |
dc.description | This document was prepared by the MicroBooNE Collaboration using the resources of 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. MicroBooNE is supported by the following: the U.S. Department of Energy, Office of Science, Offices of High Energy Physics and Nuclear Physics; the U.S. National Science Foundation; the Swiss National Science Foundation; the Science and Technology Facilities Council (STFC), part of the United Kingdom Research and Innovation; and The Royal Society (United Kingdom). Additional support for the laser calibration system and cosmic ray tagger was provided by the Albert Einstein Center for Fundamental Physics, Bern, Switzerland. | es_ES |
dc.description.abstract | We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e(-), gamma, mu(-), pi(+/-), and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep-learning-based.e search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector. | es_ES |
dc.description.sponsorship | Fermi Research Alliance, LLC (FRA) DE-AC02-07CH11359 | es_ES |
dc.description.sponsorship | United States Department of Energy (DOE) | es_ES |
dc.description.sponsorship | National Science Foundation (NSF) | es_ES |
dc.description.sponsorship | Swiss National Science Foundation (SNSF) European Commission | es_ES |
dc.description.sponsorship | Science and Technology Facilities Council (STFC), United Kingdom Research and Innovation | es_ES |
dc.description.sponsorship | Royal Society of London | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | American Physical Society | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.title | Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.1103/PhysRevD.103.092003 | |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |