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Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
- Source :
- Scopus-Elsevier, Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP, Journal of Instrumentation, 16, 1-23, Proceedings of Science, 395, pp. 1-12, JINST, JINST, 2021, 16 (07), pp.P07016. ⟨10.1088/1748-0221/16/07/P07016⟩, Journal of Instrumentation, 16(7):P07016. IOP PUBLISHING LTD, Journal of Instrumentation, 16(07):P07016. IOP Publishing Ltd., 22 Seiten : graph. Darst., Diagramme (2021). doi:10.18154/RWTH-2021-10611, Proceedings of Science, 395, 1-12, Journal of Instrumentation, 16, 07, pp. 1-23
- Publication Year :
- 2022
- Publisher :
- Sissa Medialab Srl, 2022.
-
Abstract
- 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 $10^{17}~$eV up to more than $10^{20}~$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.<br />Comment: Published version, 23 pages, 15 figures
- Subjects :
- Photon
Physics::Instrumentation and Detectors
Astronomy
Electron
01 natural sciences
High Energy Physics - Experiment
Auger
High Energy Physics - Experiment (hep-ex)
mass [cosmic radiation]
surface [detector]
Observatory
[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]
photon: cosmic radiation
Instrumentation
Mathematical Physics
Physics
AGASA
Settore FIS/01 - Fisica Sperimentale
Detector
cosmic radiation [photon]
Astrophysics::Instrumentation and Methods for Astrophysics
Monte Carlo [numerical calculations]
electromagnetic [showers]
observatory
cosmic radiation [electron]
Analysis and statistical methods
numerical calculations: Monte Carlo
Analysis and statistical method
performance
positron: cosmic radiation
atmosphere [showers]
Cherenkov detector
data analysis method
Calibration and fitting methods
Cherenkov detectors
Cluster finding
Large detector systems for particle and astroparticle physics
Pattern recognition
Cherenkov counter: water
air
neural network
Astrophysics::High Energy Astrophysical Phenomena
FOS: Physical sciences
Cosmic ray
cosmic radiation [positron]
cosmic radiation: mass
Calibration and fitting method
Nuclear physics
statistical analysis
0103 physical sciences
showers: electromagnetic
ddc:530
ddc:610
High Energy Physics
010306 general physics
Zenith
Pierre Auger Observatory
cosmic radiation [muon]
Muon
showers: atmosphere
010308 nuclear & particles physics
detector: surface
hep-ex
water [Cherenkov counter]
electron: cosmic radiation
Recurrent neural network
muon: cosmic radiation
Large detector systems for particle and astroparticle physic
Experimental High Energy Physics
High Energy Physics::Experiment
RAIOS CÓSMICOS
experimental results
Subjects
Details
- Language :
- English
- ISSN :
- 17480221 and 18248039
- Database :
- OpenAIRE
- Journal :
- Scopus-Elsevier, Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP, Journal of Instrumentation, 16, 1-23, Proceedings of Science, 395, pp. 1-12, JINST, JINST, 2021, 16 (07), pp.P07016. ⟨10.1088/1748-0221/16/07/P07016⟩, Journal of Instrumentation, 16(7):P07016. IOP PUBLISHING LTD, Journal of Instrumentation, 16(07):P07016. IOP Publishing Ltd., 22 Seiten : graph. Darst., Diagramme (2021). doi:10.18154/RWTH-2021-10611, Proceedings of Science, 395, 1-12, Journal of Instrumentation, 16, 07, pp. 1-23
- Accession number :
- edsair.doi.dedup.....ca5f51ed49544aed59b03fdfbb4e7ef3