A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F20%3A00546314" target="_blank" >RIV/68378271:_____/20:00546314 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/e22111216" target="_blank" >https://doi.org/10.3390/e22111216</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/e22111216" target="_blank" >10.3390/e22111216</a>
Alternative languages
Result language
angličtina
Original language name
A comparative analysis of machine learning techniques for muon count in UHECR extensive air-showers
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10303 - Particles and field physics
Result continuities
Project
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Continuities
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Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Entropy
ISSN
1099-4300
e-ISSN
1099-4300
Volume of the periodical
22
Issue of the periodical within the volume
11
Country of publishing house
CH - SWITZERLAND
Number of pages
17
Pages from-to
1216
UT code for WoS article
000592764300001
EID of the result in the Scopus database
2-s2.0-85094565101