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Title:Deep-learning-based cosmic-ray mass reconstruction using the water-Cherenkov and scintillation detectors of AugerPrime
Authors:ID Langner, Niklas (Author)
ID Filipčič, Andrej (Author)
ID Lundquist, Jon Paul (Author)
ID Shivashankara, Shima Ujjani (Author)
ID Stanič, Samo (Author)
ID Vorobiov, Serguei (Author)
ID Zavrtanik, Danilo (Author)
ID Zavrtanik, Marko (Author), et al.
Files:.pdf ICRC2023_371.pdf (2,93 MB)
MD5: 5355E21E1D6E94E8126CDD0DE7F7FD01
 
URL https://pos.sissa.it/444/
 
URL https://pos.sissa.it/444/371/pdf
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:UNG - University of Nova Gorica
Abstract: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.
Keywords:Pierre Auger Observatory, ultra-high energy cosmic rays, muons, extensive air showers, surface detectors, AugerPrime, deep learning techiniques
Publication status:Published
Publication version:Version of Record
Publication date:01.01.2023
Year of publishing:2023
Number of pages:str. 1-13
PID:20.500.12556/RUNG-8799 New window
COBISS.SI-ID:182166787 New window
UDC:52
ISSN on article:1824-8039
DOI:10.22323/1.444.0371 New window
NUK URN:URN:SI:UNG:REP:SFGQ1ZWK
Publication date in RUNG:23.01.2024
Views:439
Downloads:7
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Record is a part of a proceedings

Title:38th International Cosmic Ray Conference [also] ICRC2023
COBISS.SI-ID:162195971 New window

Record is a part of a journal

Title:Proceedings of science
Shortened title:Pos proc. sci.
Publisher:Sissa
ISSN:1824-8039
COBISS.SI-ID:20239655 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P1-0031
Name:Večglasniška astrofizika

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

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