Repozitorij Univerze v Novi Gorici

Izpis gradiva
A+ | A- | Pomoč | SLO | ENG

Naslov:Deep-learning-based cosmic-ray mass reconstruction using the water-Cherenkov and scintillation detectors of AugerPrime
Avtorji:ID Langner, Niklas (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_371.pdf (2,93 MB)
MD5: 5355E21E1D6E94E8126CDD0DE7F7FD01
 
URL https://pos.sissa.it/444/
 
URL https://pos.sissa.it/444/371/pdf
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis:At the highest energies, cosmic rays can be detected only indirectly by the extensive air showers they create upon interaction with the Earth’s atmosphere. While high-statistics measurements of the energy and arrival directions of cosmic rays can be performed with large surface detector arrays like the Pierre Auger Observatory, the determination of the cosmic-ray mass on an event-by-event basis is challenging. Meaningful physical observables in this regard include the depth of maximum of air-shower profiles, which is related to the mean free path of the cosmic ray in the atmosphere and the shower development, as well as the number of muons that rises with the number of nucleons in a cosmic-ray particle. In this contribution, we present an approach to determine both of these observables from combined measurements of water-Cherenkov detectors and scintillation detectors, which are part of the AugerPrime upgrade of the Observatory. To characterize the time-dependent signals of the two detectors both separately as well as in correlation to each other, we apply deep learning techniques. Transformer networks employing the attention mechanism are especially well-suited for this task. We present the utilized network concepts and apply them to simulations to determine the precision of the event-by-event mass reconstruction that can be achieved by the combined measurements of the depth of shower maximum and the number of muons.
Ključne besede:Pierre Auger Observatory, ultra-high energy cosmic rays, muons, extensive air showers, surface detectors, AugerPrime, deep learning techiniques
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-8799 Novo okno
COBISS.SI-ID:182166787 Novo okno
UDK:52
ISSN pri članku:1824-8039
DOI:10.22323/1.444.0371 Novo okno
NUK URN:URN:SI:UNG:REP:SFGQ1ZWK
Datum objave v RUNG:23.01.2024
Število ogledov:1292
Število prenosov:9
Metapodatki:XML DC-XML DC-RDF
:
Kopiraj citat
  
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
Objavi na:Bookmark and Share


Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše podrobnosti ali sproži prenos.

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.

Nazaj