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Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
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SYSNO ASEP 0550820 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks Author(s) Aab, A. (NL)
Abreu, P. (PT)
Aglietta, M. (IT)
Bakalová, Alena (FZU-D) ORCID
Blažek, Jiří (FZU-D) ORCID, RID
Boháčová, Martina (FZU-D) RID, ORCID
Chudoba, Jiří (FZU-D) RID, ORCID
Ebr, Jan (FZU-D) RID, ORCID
Hamal, Petr (FZU-D) ORCID
Janeček, Petr (FZU-D) RID, ORCID
Juryšek, Jakub (FZU-D) ORCID
Mandát, Dušan (FZU-D) RID, ORCID
Palatka, Miroslav (FZU-D) RID, ORCID, SAI
Pech, Miroslav (FZU-D) RID, ORCID
Prouza, Michael (FZU-D) RID, ORCID
Řídký, Jan (FZU-D) RID, ORCID
dos Santos, Eva M. Martins (FZU-D) ORCID
Schovánek, Petr (FZU-D) RID, ORCID
Tobiška, Petr (FZU-D) ORCID
Trávníček, Petr (FZU-D) RID, ORCID
Vícha, Jakub (FZU-D) RID, ORCID
Yushkov, Alexey (FZU-D) ORCIDNumber of authors 372 Article number P07016 Source Title Journal of Instrumentation. - : Institute of Physics Publishing - ISSN 1748-0221
Roč. 16, č. 7 (2021)Number of pages 22 s. Language eng - English Country GB - United Kingdom Keywords analysis and statistical methods ; Cherenkov detectors ; large detector systems for particle and astroparticle physics ; pattern recognition ; cluster fin Subject RIV BF - Elementary Particles and High Energy Physics OECD category Particles and field physics R&D Projects LTT18004 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) EF18_046/0016010 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) EF16_013/0001402 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Research Infrastructure AUGER-CZ II - 90102 - Fyzikální ústav AV ČR, v. v. i.
AUGER-CZ - 90038 - Fyzikální ústav AV ČR, v. v. i.Method of publishing Limited access Institutional support FZU-D - RVO:68378271 UT WOS 000702560000001 EID SCOPUS 85110748213 DOI 10.1088/1748-0221/16/07/P07016 Annotation The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 10(17) eV up to more than 10(20) eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks.
Workplace Institute of Physics Contact Kristina Potocká, potocka@fzu.cz, Tel.: 220 318 579 Year of Publishing 2022 Electronic address https://doi.org/10.1088/1748-0221/16/07/P07016
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