A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers
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AuthorGuillén Perales, Alberto; Martínez, José Carlos; Carceller López, Juan Miguel; Herrera Maldonado, Luis Javier
Machine learningPierre Auger ObservatoryMuon countRegressionLS-SVM
Guillén, A., Martínez, J., Carceller, J. M., & Herrera, L. J. (2020). A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers. Entropy, 22(11), 1216. [doi:10.3390/e22111216]
SponsorshipSpanish Ministry of Economy and Competitiveness-MINECO FPA2015-70420-C2-2-R FPA2017-85197-P RTI2018-101674-B-I00; European Union (EU) FPA2015-70420-C2-2-R FPA2017-85197-P RTI2018-101674-B-I00
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.