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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Deep learning techniques applied to the physics of extensive air showers

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Author(s):
Guillen, A. [1] ; Bueno, A. [2, 3] ; Carceller, J. M. [2, 3] ; Martinez-Velazquez, J. C. [2, 3] ; Rubio, G. [2, 3] ; Todero Peixoto, C. J. [2, 3, 4] ; Sanchez-Lucas, P. [2, 3, 5]
Total Authors: 7
Affiliation:
[1] Univ Granada, Dept Arquitectura & Tecnol Comp, Granada - Spain
[2] Univ Granada, Dept Fis Teor & Cosmos, Granada - Spain
[3] Univ Granada, CAFPE, Granada - Spain
[4] Univ Sao Paulo, Escola Engn Lorena, Dept Ciencias Basicas & Ambientais, Sao Paulo - Brazil
[5] Univ Zurich, Zurich - Switzerland
Total Affiliations: 5
Document type: Journal article
Source: Astroparticle Physics; v. 111, p. 12-22, SEP 2019.
Web of Science Citations: 1
Abstract

Deep neural networks are a powerful technique that have found ample applications in several branches of physics. In this work, we apply deep neural networks to a specific problem of cosmic ray physics: the estimation of the muon content of extensive air showers when measured at the ground. As a working case, we explore the performance of a deep neural network applied to large sets of simulated signals recorded for the water-Cherenkov detectors of the Surface Detector of the Pierre Auger Observatory. The inner structure of the neural network is optimized through the use of genetic algorithms. To obtain a prediction of the recorded muon signal in each individual detector, we train neural networks with a mixed sample of simulated events that contain light, intermediate and heavy nuclei. When true and predicted signals are compared at detector level, the primary values of the Pearson correlation coefficients are above 95%. The relative errors of the predicted muon signals are below 10% and do not depend on the event energy, zenith angle, total signal size, distance range or the hadronic model used to generate the events. (C) 2019 Published by Elsevier B.V. (AU)

FAPESP's process: 16/19764-9 - Studies of mass composition and hadronic interactions using the arrival times of the shower particles detected with the Surface Array of the Pierre Auger Observatory
Grantee:Carlos Jose Todero Peixoto
Support type: Scholarships abroad - Research