Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F21%3A00550827" target="_blank" >RIV/68378271:_____/21:00550827 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/21:10439714 RIV/61989592:15310/21:73608967
Result on the web
<a href="http://hdl.handle.net/11104/0326140" target="_blank" >http://hdl.handle.net/11104/0326140</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1748-0221/16/07/P07019" target="_blank" >10.1088/1748-0221/16/07/P07019</a>
Alternative languages
Result language
angličtina
Original language name
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
Original language description
The atmospheric depth of the air shower maximum X-max is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of X-max are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of X-max from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of X-max The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory.
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
—
OECD FORD branch
10303 - Particles and field physics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Instrumentation
ISSN
1748-0221
e-ISSN
1748-0221
Volume of the periodical
16
Issue of the periodical within the volume
7
Country of publishing house
GB - UNITED KINGDOM
Number of pages
28
Pages from-to
P07019
UT code for WoS article
000702560000003
EID of the result in the Scopus database
2-s2.0-85111106773