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Title:Search for EeV photon-induced events at the Telescope Array
Authors:ID Kharuk, I. (Author)
ID Abbasi, R. U. (Author)
ID Abe, Y. (Author)
ID Abu-Zayyad, T. (Author)
ID Allen, M. (Author)
ID Arai, Yasuhiko (Author)
ID Arimura, R. (Author)
ID Barcikowski, E. (Author)
ID Belz, J. W. (Author)
ID Bergman, Douglas R. (Author), et al.
Files:.pdf ICRC2023_324.pdf (543,46 KB)
MD5: 5DF55A3D080AFB17D6B3846E171BC92B
 
URL https://pos.sissa.it/444/324/
 
URL https://pos.sissa.it/444/324/pdf
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:UNG - University of Nova Gorica
Abstract:We report on the updated results on the search for photon-like-induced events in the data, collected by Telescope Array's Surface Detectors during the last 14 years. In order to search for photon-like-induced events, we trained a neural network on Monte-Carlo simulated data to distinguish between the proton-induced and photon-induced air showers. Both reconstructed composition-sensitive parameters and raw signals registered by the Surface Detectors are used as input data for the neural network. The classification threshold was optimized to provide the strongest possible constraint on the photons' flux.
Keywords:Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, photons, neural network, machine learning
Publication date:01.01.2023
Year of publishing:2023
Number of pages:str. 1-10
PID:20.500.12556/RUNG-8558-9b203742-d7a2-3407-6dae-e675393fac90 New window
COBISS.SI-ID:167662339 New window
UDC:539.1
ISSN on article:1824-8039
DOI:10.22323/1.444.0324 New window
NUK URN:URN:SI:UNG:REP:63GQRVEO
Publication date in RUNG:09.10.2023
Views:1908
Downloads:8
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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

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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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