O. E. Kalashev (Author), R. U. Abbasi (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), J. P. Lundquist (Author)

Abstract

A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure.

Keywords

Telescope Array;indirect detection;surface detection;machine learning;neural network;ground array;ultra-high energy;cosmic rays;energy;arrival directions;reconstruction;

Data

Language: English
Year of publishing:
Typology: 1.08 - Published Scientific Conference Contribution
Organization: UNG - University of Nova Gorica
UDC: 539.1
COBISS: 167027459 Link will open in a new window
ISSN: 1824-8039
Views: 17
Downloads: 0
Average score: 0 (0 votes)
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Other data

Type (COBISS): Not categorized
Pages: str. 1-10
Chronology: 2022
DOI: 10.22323/1.395.0252
ID: 20033968