Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Journal Article
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· Eur.Phys.J.C
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- U. Fed. Alfenas
- Cracow, INP
- UCLA
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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.
- Research Organization:
- ABC Federal U.; APC, Paris; Abilene Christian U.; Ahmedabad, Phys. Res. Lab; Akdeniz U.; Annecy, LAPP; Antananarivo U.; Antonio Narino U.; Argonne National Laboratory (ANL), Argonne, IL (United States); Arizona U.; Asuncion Natl. U.; Athens U.; Augustana Coll., Sioux Falls; Banaras Hindu U.; Barcelona, IFAE; Basel U.; Bern U.; Beykent U.; Birmingham U.; Boston U.; Bristol U.; Brookhaven National Laboratory (BNL), Upton, NY (United States); Bucharest U.; CERN; CINVESTAV, IPN; CTU, Prague; Calcutta, VECC; Caltech; Cambridge U.; Catolica del Norte U.; Charles U.; Chicago U.; Chung-Ang U.; Cincinnati U.; Colima U.; Colorado State U.; Colorado U.; Columbia U.; Cracow, INP; Dakota State U.; Dallas U.; Daresbury; Diadema, Sao Paulo Fed. U.; Drexel U.; Dubna, JINR; Duke U.; Durham U.; Edinburgh U.; Escuela de Ingenieria de Antioquia; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Florida U.; GSSI, Aquila; GTU, Tbilisi; Goias U.; Gran Sasso; Granada U.; Guanajuato U.; Harish-Chandra Res. Inst.; Harvard U.; Hawaii U.; Houston U.; Hyderabad U.; IIT, Chicago; INFN, Bologna; INFN, Catania; INFN, Ferrara; INFN, LNS; INFN, Lecce; INFN, Milan; INFN, Milan Bicocca; INFN, Naples; INFN, Padua; IP2I, Lyon; IPM, Tehran; ITPM, Yerevan; Idaho State U.; Imperial Coll., London; Indian Inst. Tech., Guwahati; Indian Inst. Tech., Hyderabad; Indiana U.; Iowa State U.; Iowa U.; Iwate U.; Jammu U.; Jeonbuk Natl. U.; Jyvaskyla U.; K L U.; KEK, Tsukuba; KISTI, Daejeon; Kansas State U.; Kure Tech. Coll.; LIP, Lisbon; LPSC, Grenoble; Lancaster U.; Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Lima, Pont. U. Catolica; Liverpool U.; Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Louisiana State U.; Lucknow U.; MIT; Madrid, Autonoma U.; Madrid, CIEMAT; Manchester U.; Medellin U.; Michigan State U.; Michigan U.; Minnesota U.; Minnesota U., Duluth; Mississippi U.; Moscow, INR; Nehru U.; New Mexico U.; Nikhef, Amsterdam; Niteroi, Fluminense U.; North Dakota U.; Northern Illinois U.; Northwestern U.; Notre Dame U.; Occidental Coll.; Ohio State U.; Oregon State U.; Orsay, LAL; Oxford U.; Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Padua U.; Panjab U.; Pavia U.; Penn State U.; Pennsylvania U.; Pisa U.; Pittsburgh U.; Prague, Inst. Phys.; Puerto Rico U., Mayaguez; Punjab Agric. U., Ludhiana; Queen Mary, U. of London; Rio de Janeiro Federal U.; Rio de Janeiro, CBPF; Rochester U.; Royal Holloway, U. of London; Rutgers U., Piscataway; Rutherford; SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); SUNY, Albany; SUNY, Stony Brook; SYSU, Guangzhou; Saclay; Salento U.; San Jose State U.; Sanford Underground Lab.; Santiago de Compostela U., IGFAE; Sao Carlos Federal U.; Sheffield U.; South Carolina U.; South Dakota Sch. Mines Tech.; South Dakota State U.; Southern Methodist U.; Sussex U.; Syracuse U.; Taras Shevchenko U.; Tech. Fed. Parana U.; Texas A-M; Texas A-M, Corpus Christi; Texas U.; Texas U., Arlington; Tokyo U., IPMU; Toronto U.; Tufts U.; U. Atlantico, Barranquilla; U. Campinas; U. Fed. Alfenas; U. Sergio Arboleda, Bogota; UC, Berkeley; UC, Davis; UC, Irvine; UC, Riverside; UC, Santa Barbara; UCLA; UNI, Lima; UNIST, Ulsan; Univ. of Rochester, NY (United States); University Coll. London; Valencia U., IFIC; Valley City State U.; Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Virginia Tech.; Warsaw U.; Warwick U.; Wellesley Coll.; Wichita State U.; William-Mary Coll.; Wisconsin U., Madison; Yale U.; York U., Canada; Zurich, ETH
- Sponsoring Organization:
- US Department of Energy; USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Organization:
- DUNE Collaboration; DUNE collaboration
- Grant/Contract Number:
- AC02-05CH11231; AC02-07CH11359; SC0007859; SC0008475; SC0023471
- OSTI ID:
- 1873147
- Report Number(s):
- CERN-EP-2022-077; FERMILAB-PUB-22-240-AD-ESH-LBNF-ND-SCD; arXiv:2203.17053; oai:inspirehep.net:2060793; arXiv:2203.17053
- Journal Information:
- Eur.Phys.J.C, Journal Name: Eur.Phys.J.C Journal Issue: 10 Vol. 82
- Country of Publication:
- United States
- Language:
- English
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