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Naslov:Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning
Avtorji:ID Glombitza, J. (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_359.pdf (1,66 MB)
MD5: 0DAD1A556E2885654C4A081990DCE57A
 
URL https://pos.sissa.it/395/359/
 
URL https://pos.sissa.it/395/359/pdf
 
Jezik:Angleški jezik
Vrsta gradiva:Delo ni kategorizirano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis:The measurement of the mass composition of ultra-high energy cosmic rays constitutes a prime challenge in astroparticle physics. Most detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights. Using deep neural networks, it is now possible for the first time to perform an event-by-event reconstruction of Xmax with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on Xmax, and thus, the cosmic-ray composition. Since the SD operates with a duty cycle of almost 100% and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above 10^19.5 eV. In this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models, verify its functionality using Auger hybrid measurements, and find that the method can extract mass information on an event level.
Ključne besede:Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, neural network, machine learning
Status publikacije:Objavljeno
Leto izida:2022
PID:20.500.12556/RUNG-8482 Novo okno
COBISS.SI-ID:166307331 Novo okno
DOI:10.22323/1.395.0359 Novo okno
NUK URN:URN:SI:UNG:REP:8MB7LSVI
Datum objave v RUNG:29.09.2023
Število ogledov:785
Število prenosov:5
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Gradivo je del monografije

Naslov:37th International Cosmic Ray Conference : ICRC2023
Kraj izida:Trieste, Italy
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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