I. Kharuk (Author), R. U. Abbasi (Author), Y. Abe (Author), T. Abu-Zayyad (Author), M. Allen (Author), Yasuhiko Arai (Author), R. Arimura (Author), E. Barcikowski (Author), J. W. Belz (Author), D. R. Bergman (Author)

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;

Data

Language: English
Year of publishing:
Typology: 1.08 - Published Scientific Conference Contribution
Organization: UNG - University of Nova Gorica
UDC: 539.1
COBISS: 167662339 Link will open in a new window
ISSN: 1824-8039
Views: 306
Downloads: 5
Average score: 0 (0 votes)
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Other data

Type (COBISS): Not categorized
Pages: str. 1-10
Chronology: 2023
DOI: 10.22323/1.444.0324
ID: 21815689