A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers

0546314 - FZÚ 2022 RIV CH eng J - Journal Article
Guillen, A. … Total 4 authors
A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers.
Entropy. Roč. 22, č. 11 (2020), č. článku 1216. E-ISSN 1099-4300
Research Infrastructure: AUGER-CZ II - 90102
Keywords : machine learning * Pierre Auger Observatory * muon count
OECD category: Particles and field physics
Impact factor: 2.524, year: 2020
Method of publishing: Open access
https://doi.org/10.3390/e22111216

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.
Permanent Link: http://hdl.handle.net/11104/0322849