Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks

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dc.citation.titleJournal of Instrumentation (JINST)es
dc.citation.volume16
dc.creatorFreire, M. M.
dc.creatorThe Pierre Auger collaboration
dc.date.accessioned2022-04-12T20:12:20Z
dc.date.available2022-04-12T20:12:20Z
dc.date.issued2021-07-12
dc.descriptionThe Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017 eV up to more than 1020 eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.es
dc.description.filFil: Freire, M. M. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Física de Rosario (IFIR-CONICET). Argentina.es
dc.description.sponsorshipDépartement Physique Nucléaire et Corpusculaire: PNC-IN2P3/CNRS
dc.description.sponsorshipDépartement Sciences de l’Univers
dc.description.sponsorshipMaría de Maeztu Unit of Excellence: MDM-2016-0692
dc.description.sponsorshipMinistero degli Affari Esteri
dc.description.sponsorshipRENATA Red Nacional Temática de Astropartículas: FPA2015-68783-REDT
dc.description.sponsorshipSURF
dc.description.sponsorshipNational Science Foundation (NSF)
dc.description.sponsorshipU.S. Department of Energy (USDOE): DE-AC02-07CH11359, DE-FG02-99ER41107, DE-FR02-04ER41300, DE-SC0011689
dc.description.sponsorshipDirectorate for Mathematical and Physical Sciences (MPS): Directorate for Mathematical and Physical Sciences
dc.description.sponsorshipUnited Nations Educational, Scientific and Cultural Organization (UNESCO)
dc.description.sponsorshipAustralian Research Council (ARC)
dc.description.sponsorshipHelmholtz-Gemeinschaft (HGF)
dc.description.sponsorshipDeutsche Forschungsgemeinschaft (DFG)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP): 1999/05404-3, 2010/07359-6, 2019/10151-2
dc.description.sponsorshipFundação para a Ciência e a Tecnologia (FCT)
dc.description.sponsorshipBundesministerium für Bildung und Forschung (BMBF)
dc.description.sponsorshipComisión Nacional de Energía Atómica (CNEA)
dc.description.sponsorshipConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
dc.description.sponsorshipFederación Española de Enfermedades Raras (FEDER)
dc.description.sponsorshipAgencia Nacional de Promoción Científica y Tecnológica (ANPCyT)
dc.description.sponsorshipConsejo Nacional de Ciencia y Tecnología (CONACYT): 167733
dc.description.sponsorshipMinisterie van Onderwijs, Cultuur en Wetenschap (OCW)
dc.description.sponsorshipNederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)
dc.description.sponsorshipMinistero dell’Istruzione, dell’Università e della Ricerca (MIUR)
dc.description.sponsorshipMinisterium für Wissenschaft, Forschung und Kunst Baden-Württemberg (MWK)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ)
dc.description.sponsorshipConseil Régional, Île-de-France
dc.description.sponsorshipInstituto Nazionale di Fisica Nucleare (INFN)
dc.description.sponsorshipNarodowe Centrum Nauki (NCN): 2013/08/M/ST9/00322, 2016/23/B/ST9/01635, 5-2013/10/M/ST9/00062, UMO-2016/22/M/ST9/00198
dc.description.sponsorshipJavna Agencija za Raziskovalno Dejavnost RS (ARRS): I0-0033, N1-0111, P1-0031, P1-0385
dc.description.sponsorshipMinisterstwo Edukacji i Nauki (MNiSW9: DIR/WK/2018/11
dc.description.sponsorshipFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)
dc.description.sponsorshipCentre National de la Recherche Scientifique (CNRS)
dc.description.sponsorshipFinanciadora de Estudos e Projetos (FINEP)
dc.description.sponsorshipIstituto Nazionale di Astrofisica (INAF)
dc.description.sponsorshipUniversidad Nacional Autónoma de México (UNAM)
dc.description.sponsorshipMinistry of Education and Research: PN-III-P1-1.2-PCCDI-2017-0839/19PCCDI/2018, PN19060102, PN19150201/16N/2019
dc.description.sponsorshipEuropean Regional Development Fund (ERDF)
dc.description.sponsorshipMinisterium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen (MIWF-NRW)
dc.description.sponsorshipMinisterio de Asuntos Económicos y Transformación Digital (MINECO): FPA2017-85114-P, PID2019-104676GB-C32
dc.description.sponsorshipXunta de Galicia: ED431C 2017/07
dc.description.sponsorshipJunta de Andalucía: P18-FR-4314
dc.description.sponsorshipMinistério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC): CZ.02.1.01/0.0/0.0/16_013/0001402, CZ.02.1.01/0.0/0.0/17_049/0008422, CZ.02.1.01/0.0/0.0/18_046/0016010, LM2015038, LM2018102, MSMT CR LTT18004
dc.description.sponsorshipPrograma Operacional Temático Factores de Competitividade (POFC)
dc.description.sponsorshipInstitut Lagrange de Paris (ILP)
dc.formatapplication/pdf
dc.format.extent1-21es
dc.identifier.issn1748-0221es
dc.identifier.urihttp://hdl.handle.net/2133/23413
dc.language.isoenges
dc.publisherIOP Publishinges
dc.relation.publisherversionhttps://iopscience.iop.org/article/10.1088/1748-0221/16/07/P07016es
dc.relation.publisherversionhttps://doi.org/10.1088/1748-0221/16/07/P07016
dc.rightsopenAccesses
dc.rights.holderFreire, M. M.es
dc.rights.holderThe Pierre Auger collaboration es
dc.rights.textAtribución 3.0 No portada (CC BY 3.0)es
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/deed.es*
dc.subjectAnalysis and statistical methodses
dc.subjectCherenkov detectorses
dc.subjectLarge detector systems for particle and astroparticle physicses
dc.subjectPattern recognition, cluster finding, calibration and fitting methodses
dc.titleExtraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networkses
dc.typepublishedVersion
dc.typearticle
dc.typeartículo
dc.type.collectionarticulo
dc.type.versionpublishedVersion

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