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

0564677 - FZÚ 2023 RIV IT eng C - Conference Paper (international conference)
Abreu, A. - Aglietta, M. - Albury, J.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 373 authors
Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks.
Proceedings of Science. Vol. 395. Trieste: Sissa Medilab srl, 2022, č. článku 229. ISSN 1824-8039.
[International Cosmic Ray Conference /37./. Berlin (DE), 12.07.2021-23.07.2021]
R&D Projects: GA MŠMT(CZ) LM2018102
Institutional support: RVO:68378271
Keywords : muons * ultra-high energies
OECD category: Particles and field physics
https://pos.sissa.it/395/229/pdf

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies.
Permanent Link: https://hdl.handle.net/11104/0336327