Repozitorij Univerze v Novi Gorici

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Naslov:Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger Observatory
Avtorji:ID Ellwanger, Fiona (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_275.pdf (1,78 MB)
MD5: DB6EB91F0B1F00B2CF58A2EDFE967947
 
URL https://pos.sissa.it/444/
 
URL https://pos.sissa.it/444/275/pdf
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis:Exploring physics at energies beyond the reach of human-built accelerators by studying cosmic rays requires an accurate reconstruction of their energy. At the highest energies, cosmic rays are indirectly measured by observing a shower of secondary particles produced by their interaction in the atmosphere. At the Pierre Auger Observatory, the energy of the primary particle is either reconstructed from measurements of the emitted fluorescence light, produced when secondary particles travel through the atmosphere, or shower particles detected with the surface detector at the ground. The surface detector comprises a triangular grid of water-Cherenkov detectors that measure the shower footprint at the ground level. With deep learning, large simulation data sets can be used to train neural networks for reconstruction purposes. In this work, we present an application of a neural network to estimate the energy of the primary particle from the surface detector data by exploiting the time structure of the particle footprint. When evaluating the precision of the method on air shower simulations, we find the potential to significantly reduce the composition bias compared to methods based on fitting the lateral signal distribution. Furthermore, we investigate possible biases arising from systematic differences between simulations and data.
Ključne besede:ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
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-8784 Novo okno
COBISS.SI-ID:182030339 Novo okno
UDK:52
ISSN pri članku:1824-8039
DOI:10.22323/1.444.0275 Novo okno
NUK URN:URN:SI:UNG:REP:FYWMIJVD
Datum objave v RUNG:22.01.2024
Število ogledov:493
Število prenosov:4
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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