Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

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dc.audiencepublishedVersion
dc.citation.titleJournal of Instrumentationes
dc.citation.volume16
dc.creatorFreir, M. M.
dc.creatorMicheletti, M. I.
dc.creatorThe Pierre Auger collaboration
dc.date.accessioned2022-04-07T16:00:01Z
dc.date.available2022-04-07T16:00:01Z
dc.date.issued2021-07-14
dc.descriptionThe atmospheric depth of the air shower maximum xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long shortterm memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV.es
dc.description.filFil: Freir, M. M. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Física de Rosario (IFIR-CONICET); Argentina.es
dc.description.filFil: Micheletti, M. I. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Física de Rosario (IFIR-CONICET); Argentina.es
dc.formatapplication/pdf
dc.format.extent1-27es
dc.identifier.issn1748-0221es
dc.identifier.urihttp://hdl.handle.net/2133/23338
dc.language.isoenges
dc.publisherIOP Publishinges
dc.rightsopenAccesses
dc.rights.holderThe Pierre Auger collaborationes
dc.rights.holderFreir, M. M.es
dc.rights.holderMicheletti, M. I.es
dc.rights.textAttribution 4.0 International (CC BY 4.0)es
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/ar/*
dc.subjectData analysises
dc.subjectPattern recognitiones
dc.subjectCluster findinges
dc.subjectCalibration and fitting methodses
dc.subjectLarge detector systems for particle and astroparticle physicses
dc.subjectParticle identification methodses
dc.titleDeep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatoryes
dc.typepublishedVersion
dc.typearticle
dc.typeartículo
dc.type.collectionarticulo
dc.type.versionpublishedVersion

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(FBIOyF) Departamento de Química Física - Artículo de Revista