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Article

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

1
Computer Technology and Architecture, University of Granada, 18071 Granada, Spain
2
Cosmos and Theoretical Physics Department, Univerisity of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(11), 1216; https://doi.org/10.3390/e22111216
Received: 7 September 2020 / Revised: 9 October 2020 / Accepted: 18 October 2020 / Published: 26 October 2020
The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity. View Full-Text
Keywords: machine learning; Pierre Auger Observatory; muon count; regression; LSSVM machine learning; Pierre Auger Observatory; muon count; regression; LSSVM
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MDPI and ACS Style

Guillén, A.; Martínez, J.; Carceller, J.M.; Herrera, L.J. A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers. Entropy 2020, 22, 1216. https://doi.org/10.3390/e22111216

AMA Style

Guillén A, Martínez J, Carceller JM, Herrera LJ. A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers. Entropy. 2020; 22(11):1216. https://doi.org/10.3390/e22111216

Chicago/Turabian Style

Guillén, Alberto, José Martínez, Juan M. Carceller, and Luis J. Herrera 2020. "A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers" Entropy 22, no. 11: 1216. https://doi.org/10.3390/e22111216

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