Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks

0550820 - FZÚ 2022 RIV GB eng J - Journal Article
Aab, A. - Abreu, P. - Aglietta, M. - Bakalová, Alena - Blažek, Jiří - Boháčová, Martina - Chudoba, Jiří - Ebr, Jan - Hamal, Petr - Janeček, Petr - Juryšek, Jakub - Mandát, Dušan - Palatka, Miroslav - Pech, Miroslav - Prouza, Michael - Řídký, Jan - dos Santos, Eva M. Martins - Schovánek, Petr - Tobiška, Petr - Trávníček, Petr - Vícha, Jakub - Yushkov, Alexey … Total 372 authors
Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks.
Journal of Instrumentation. Roč. 16, č. 7 (2021), č. článku P07016. ISSN 1748-0221. E-ISSN 1748-0221
R&D Projects: GA MŠMT LTT18004; GA MŠMT(CZ) EF18_046/0016010; GA MŠMT EF16_013/0001402
Grant - others:OP VVV - AUGERII.CZ(XE) CZ.02.1.01/0.0/0.0/18_046/0016010; OP VVV - AUGER-CZ(XE) CZ.02.1.01/0.0/0.0/16_013/0001402
Research Infrastructure: AUGER-CZ II - 90102; AUGER-CZ - 90038
Institutional support: RVO:68378271
Keywords : analysis and statistical methods * Cherenkov detectors * large detector systems for particle and astroparticle physics * pattern recognition * cluster fin
OECD category: Particles and field physics
Impact factor: 1.121, year: 2021
Method of publishing: Limited access
https://doi.org/10.1088/1748-0221/16/07/P07016

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.

Permanent Link: http://hdl.handle.net/11104/0326127