Repozitorij Univerze v Novi Gorici

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Naslov:Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
Avtorji:ID Carceller, J.M. (Avtor)
ID Filipčič, Andrej (Avtor)
ID Lundquist, Jon Paul (Avtor)
ID Stanič, Samo (Avtor)
ID Vorobiov, Serguei (Avtor)
ID Zavrtanik, Danilo (Avtor)
ID Zavrtanik, Marko (Avtor)
ID Zehrer, Lukas (Avtor), et al.
Datoteke:.pdf ICRC2021_229.pdf (1,08 MB)
MD5: 96FD77267D94FF660D6FB427D788289B
 
URL https://pos.sissa.it/395/229/
 
URL https://pos.sissa.it/395/229/pdf
 
Jezik:Angleški jezik
Vrsta gradiva:Delo ni kategorizirano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis: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. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 10^18.5 eV and 10^20 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration.
Ključne besede:Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, muons, machine learning, recurrent neural network
Status publikacije:Objavljeno
Leto izida:2022
PID:20.500.12556/RUNG-8541 Novo okno
COBISS.SI-ID:167053827 Novo okno
DOI:10.22323/1.395.0229 Novo okno
NUK URN:URN:SI:UNG:REP:BSVQWKRJ
Datum objave v RUNG:04.10.2023
Število ogledov:648
Število prenosov:6
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del monografije

Naslov:37th International Cosmic Ray Conference : ICRC2023
Kraj izida:Trieste, Italy
Založnik:Proceedings of Science
Leto izida:2022

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P1-0031
Naslov:Večglasniška astrofizika

Licence

Licenca:CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
Opis:Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

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