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

The 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 extensi...

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Autores principales: Freire, M. M., The Pierre Auger Observatory
Formato: article artículo publishedVersion
Lenguaje:Inglés
Publicado: IOP Publishing 2022
Materias:
Analysis and statistical methods
Cherenkov detectors
Large detector systems for particle and astroparticle physics
Pattern recognition, cluster finding, calibration and fitting methods
Acceso en línea:http://hdl.handle.net/2133/23413
http://hdl.handle.net/2133/23413
Aporte de:
Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR) de Universidad Nacional de Rosario
id I15-R121-2133-23413
record_format dspace
spelling I15-R121-2133-234132023-05-18T18:07:28Z Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks Freire, M. M. The Pierre Auger Observatory Analysis and statistical methods Cherenkov detectors Large detector systems for particle and astroparticle physics Pattern recognition, cluster finding, calibration and fitting methods The 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. Fil: Freire, M. M. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Física de Rosario (IFIR-CONICET). Argentina. 2022-04-12T20:12:20Z 2022-04-12T20:12:20Z 2021-07-12 2022-04-12T20:12:20Z 2022-04-12T20:12:20Z 2021-07-12 article artículo publishedVersion 1748-0221 http://hdl.handle.net/2133/23413 http://hdl.handle.net/2133/23413 eng https://iopscience.iop.org/article/10.1088/1748-0221/16/07/P07016 https://creativecommons.org/licenses/by/3.0/deed.es Freire, M. M. The Pierre Auger Observatory Atribución 3.0 No portada (CC BY 3.0) openAccess application/pdf IOP Publishing
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
topic Analysis and statistical methods
Cherenkov detectors
Large detector systems for particle and astroparticle physics
Pattern recognition, cluster finding, calibration and fitting methods
spellingShingle Analysis and statistical methods
Cherenkov detectors
Large detector systems for particle and astroparticle physics
Pattern recognition, cluster finding, calibration and fitting methods
Freire, M. M.
The Pierre Auger Observatory
Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
topic_facet Analysis and statistical methods
Cherenkov detectors
Large detector systems for particle and astroparticle physics
Pattern recognition, cluster finding, calibration and fitting methods
description The 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.
format article
artículo
publishedVersion
author Freire, M. M.
The Pierre Auger Observatory
author_facet Freire, M. M.
The Pierre Auger Observatory
author_sort Freire, M. M.
title Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
title_short Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
title_full Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
title_fullStr Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
title_full_unstemmed Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
title_sort extraction of the muon signals recorded with the surface detector of the pierre auger observatory using recurrent neural networks
publisher IOP Publishing
publishDate 2022
url http://hdl.handle.net/2133/23413
http://hdl.handle.net/2133/23413
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AT thepierreaugerobservatory extractionofthemuonsignalsrecordedwiththesurfacedetectorofthepierreaugerobservatoryusingrecurrentneuralnetworks
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