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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10303 - Particles and field physics

Result continuities

  • Project

  • Continuities

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