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Title:Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger Observatory
Authors:ID Ellwanger, Fiona (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_275.pdf (1,78 MB)
MD5: DB6EB91F0B1F00B2CF58A2EDFE967947
 
URL https://pos.sissa.it/444/
 
URL https://pos.sissa.it/444/275/pdf
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:UNG - University of Nova Gorica
Abstract: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.
Keywords:ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
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-8784 New window
COBISS.SI-ID:182030339 New window
UDC:52
ISSN on article:1824-8039
DOI:10.22323/1.444.0275 New window
NUK URN:URN:SI:UNG:REP:FYWMIJVD
Publication date in RUNG:22.01.2024
Views:2108
Downloads:5
Metadata:XML DC-XML DC-RDF
:
ELLWANGER, Fiona, FILIPČIČ, Andrej, LUNDQUIST, Jon Paul, SHIVASHANKARA, Shima Ujjani, STANIČ, Samo, VOROBIOV, Serguei, ZAVRTANIK, Danilo and ZAVRTANIK, Marko, 2023, Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger Observatory. In : 38th International Cosmic Ray Conference [also] ICRC2023 [online]. Published Scientific Conference Contribution. 2023. p. 1–13. [Accessed 7 April 2025]. Retrieved from: https://repozitorij.ung.si/IzpisGradiva.php?lang=eng&id=8784
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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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