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Title:Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning
Authors:ID Glombitza, J. (Author)
ID Filipčič, Andrej (Author)
ID Lundquist, Jon Paul (Author)
ID Stanič, Samo (Author)
ID Vorobiov, Serguei (Author)
ID Zavrtanik, Danilo (Author)
ID Zavrtanik, Marko (Author)
ID Zehrer, Lukas (Author), et al.
Files:.pdf ICRC2021_359.pdf (1,66 MB)
MD5: 0DAD1A556E2885654C4A081990DCE57A
 
URL https://pos.sissa.it/395/359/
 
URL https://pos.sissa.it/395/359/pdf
 
Language:English
Work type:Not categorized
Typology:1.08 - Published Scientific Conference Contribution
Organization:UNG - University of Nova Gorica
Abstract: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.
Keywords:Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, neural network, machine learning
Publication status:Published
Year of publishing:2022
PID:20.500.12556/RUNG-8482 New window
COBISS.SI-ID:166307331 New window
DOI:10.22323/1.395.0359 New window
NUK URN:URN:SI:UNG:REP:8MB7LSVI
Publication date in RUNG:29.09.2023
Views:2044
Downloads:7
Metadata:XML DC-XML DC-RDF
:
GLOMBITZA, J., FILIPČIČ, Andrej, LUNDQUIST, Jon Paul, STANIČ, Samo, VOROBIOV, Serguei, ZAVRTANIK, Danilo, ZAVRTANIK, Marko and ZEHRER, Lukas, 2022, Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning. In : 37th International Cosmic Ray Conference [online]. Published Scientific Conference Contribution. Trieste, Italy. 2022. [Accessed 7 April 2025]. Retrieved from: https://repozitorij.ung.si/IzpisGradiva.php?lang=eng&id=8482
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Record is a part of a monograph

Title:37th International Cosmic Ray Conference : ICRC2023
Place of publishing:Trieste, Italy
Year of publishing:2022

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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