Deep learning techniques applied to the physics of 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_____%2F19%3A00546579" target="_blank" >RIV/68378271:_____/19:00546579 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.astropartphys.2019.03.001" target="_blank" >https://doi.org/10.1016/j.astropartphys.2019.03.001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.astropartphys.2019.03.001" target="_blank" >10.1016/j.astropartphys.2019.03.001</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning techniques applied to the physics of extensive air showers
Original language description
Deep neural networks are a powerful technique that have found ample applications in several branches of physics. In this work, we apply deep neural networks to a specific problem of cosmic ray physics: the estimation of the muon content of extensive air showers when measured at the ground. As a working case, we explore the performance of a deep neural network applied to large sets of simulated signals recorded for the water-Cherenkov detectors of the Surface Detector of the Pierre Auger Observatory. The inner structure of the neural network is optimized through the use of genetic algorithms. To obtain a prediction of the recorded muon signal in each individual detector, we train neural networks with a mixed sample of simulated events that contain light, intermediate and heavy nuclei. When true and predicted signals are compared at detector level, the primary values of the Pearson correlation coefficients are above 95%.
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
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Astroparticle Physics
ISSN
0927-6505
e-ISSN
1873-2852
Volume of the periodical
111
Issue of the periodical within the volume
September
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
12-22
UT code for WoS article
000470047300002
EID of the result in the Scopus database
2-s2.0-85063501854