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

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The 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...

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Autores principales:	Freir, M. M., Micheletti, M. I., The Pierre Auger collaboration
Formato:	article artículo publishedVersion
Lenguaje:	Inglés
Publicado:	IOP Publishing 2022
Materias:	Data analysis Pattern recognition Cluster finding Calibration and fitting methods Large detector systems for particle and astroparticle physics Particle identification methods
Acceso en línea:	http://hdl.handle.net/2133/23338 http://hdl.handle.net/2133/23338
Aporte de:	Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR) de Universidad Nacional de Rosario

id	I15-R121-2133-23338
record_format	dspace
institution	Universidad Nacional de Rosario
institution_str	I-15
repository_str	R-121
collection	Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR)
language	Inglés
orig_language_str_mv	eng
topic	Data analysis Pattern recognition Cluster finding Calibration and fitting methods Large detector systems for particle and astroparticle physics Particle identification methods
spellingShingle	Data analysis Pattern recognition Cluster finding Calibration and fitting methods Large detector systems for particle and astroparticle physics Particle identification methods Freir, M. M. Micheletti, M. I. The Pierre Auger collaboration Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
topic_facet	Data analysis Pattern recognition Cluster finding Calibration and fitting methods Large detector systems for particle and astroparticle physics Particle identification methods
description	The 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.
format	article artículo publishedVersion
author	Freir, M. M. Micheletti, M. I. The Pierre Auger collaboration
author_facet	Freir, M. M. Micheletti, M. I. The Pierre Auger collaboration
author_sort	Freir, M. M.
title	Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
title_short	Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
title_full	Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
title_fullStr	Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
title_full_unstemmed	Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
title_sort	deep-learning based reconstruction of the shower maximum xmax using the water-cherenkov detectors of the pierre auger observatory
publisher	IOP Publishing
publishDate	2022
url	http://hdl.handle.net/2133/23338 http://hdl.handle.net/2133/23338
work_keys_str_mv	AT freirmm deeplearningbasedreconstructionoftheshowermaximumxmaxusingthewatercherenkovdetectorsofthepierreaugerobservatory AT michelettimi deeplearningbasedreconstructionoftheshowermaximumxmaxusingthewatercherenkovdetectorsofthepierreaugerobservatory AT thepierreaugercollaboration deeplearningbasedreconstructionoftheshowermaximumxmaxusingthewatercherenkovdetectorsofthepierreaugerobservatory
bdutipo_str	Repositorios
_version_	1764820412007448576

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

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