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Naslov:Reconstruction of muon number of air showers with the surface detector of the Pierre Auger Observatory using neural networks
Avtorji:ID Traugott Hahn, Steffen (Avtor)
ID Filipčič, Andrej (Avtor)
ID Lundquist, Jon Paul (Avtor)
ID Shivashankara, Shima Ujjani (Avtor)
ID Stanič, Samo (Avtor)
ID Vorobiov, Serguei (Avtor)
ID Zavrtanik, Danilo (Avtor)
ID Zavrtanik, Marko (Avtor), et al.
Datoteke:.pdf ICRC2023_318.pdf (939,38 KB)
MD5: FA59A02427C27DB37195B2E448956897
 
.pdf ICRC2023_318_a1.pdf (689,78 KB)
MD5: 99EEEE721C288B31C67E017D8420F3D9
 
URL https://pos.sissa.it/444/
 
To gradivo ima še več datotek. Celoten seznam je na voljo spodaj.
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis:To understand the physics of cosmic rays at the highest energies, it is mandatory to have an accurate knowledge of their mass composition. Since the mass of the primary particles cannot be measured directly, we have to rely on the analysis of mass-sensitive observables to gain insights into this composition. A promising observable for this purpose is the number of muons at the ground relative to that of an air shower induced by a proton primary of the same energy and inclination angle, commonly referred to as the relative muon number �μ. Due to the complexity of shower footprints, the extraction of �μ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct �μ by exploiting the spatial and temporal information of the signals induced by shower particles using neural networks. Using this data-driven approach permits us to tackle this task without the need of modeling the underlying physics and, simultaneously, gives us insights into the feasibility of such an approach. In this contribution, we summarize the progress of the deep-learning-based approach to estimate �μ using simulated surface detector data of the Pierre Auger Observatory. Instead of using single architecture, we present different network designs verifying that they reach similar results. Moreover, we demonstrate the potential for estimating �μ using the scintillator surface detector of the AugerPrime upgrade.
Ključne besede:ultra-high energy cosmic rays, Pierre Auger Observatory, AugerPrime, surface detector
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Datum objave:01.01.2023
Leto izida:2023
Št. strani:str. 1-13
PID:20.500.12556/RUNG-8791 Novo okno
COBISS.SI-ID:182069763 Novo okno
UDK:52
ISSN pri članku:1824-8039
DOI:10.22323/1.444.0318 Novo okno
NUK URN:URN:SI:UNG:REP:MZOAR7OX
Datum objave v RUNG:23.01.2024
Število ogledov:1121
Število prenosov:7
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 zbornika

Naslov:38th International Cosmic Ray Conference [also] ICRC2023
COBISS.SI-ID:162195971 Novo okno

Gradivo je del revije

Naslov:Proceedings of science
Skrajšan naslov:Pos proc. sci.
Založnik:Sissa
ISSN:1824-8039
COBISS.SI-ID:20239655 Novo okno

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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