@misc{10481/77744,
year = {2022},
month = {10},
url = {https://hdl.handle.net/10481/77744},
abstract = {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.},
organization = {Fermi Research Alliance, LLC (FRA)	DE-AC02-07CH11359},
organization = {Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)		
Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)		
Fundacao de Amparo a Pesquisa do Estado do Goias (FAPEG)		
Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)},
organization = {Canada Foundation for Innovation		
IPP, Canada		
Natural Sciences and Engineering Research Council of Canada (NSERC)},
organization = {CERN},
organization = {Ministry of Education, Youth & Sports - Czech Republic
Czech Republic Government},
organization = {ERDF, European Union		
H2020-EU, European Union		
MSCA, European Union},
organization = {Centre National de la Recherche Scientifique (CNRS)		
French Atomic Energy Commission},
organization = {Istituto Nazionale di Fisica Nucleare (INFN)},
organization = {Portuguese Foundation for Science and Technology
European Commission},
organization = {National Research Foundation of Korea},
organization = {CAM, Spain		
La Caixa Foundation		
Junta de Andalucia-FEDER, Spain		
Ministry of Science and Innovation, Spain (MICINN)
Spanish Government		
Xunta de Galicia},
organization = {SERI, Switzerland		
Swiss National Science Foundation (SNSF)},
organization = {Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)},
organization = {Royal Society of London		
UK Research & Innovation (UKRI)},
organization = {Science & Technology Facilities Council (STFC)		
United States Department of Energy (DOE)		
National Science Foundation (NSF)		
National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility	DE-AC02-05CH11231},
publisher = {Springer},
title = {Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network},
doi = {10.1140/epjc/s10052-022-10791-2},
author = {Abed Abud, A. and García Gámez, Diego and Nicolás Arnaldos, Francisco Javier and Zamorano García, Bruno and Dune Collaboration},
}