Code for paper submitted to Entropy A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers https://www.mdpi.com/1099-4300/22/11/1216

Abstract 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.

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.

@article{Guill_n_2020, title={A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers}, volume={22}, ISSN={1099-4300}, url={http://dx.doi.org/10.3390/e22111216}, DOI={10.3390/e22111216}, number={11}, journal={Entropy}, publisher={MDPI AG}, author={Guillén, Alberto and Martínez, José and Carceller, Juan Miguel and Herrera, Luis Javier}, year={2020}, month={Oct}, pages={1216}}