I. Kharuk (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), S. A. Blake (Author), J. P. Lundquist (Author)

Abstract

We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. The progress was achieved by using two neural networks. The first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events.

Keywords

Telescope Array;indirect detection;ground array;cosmic rays;surface detection;ultra-high energy;

Data

Language: English
Year of publishing:
Typology: 1.08 - Published Scientific Conference Contribution
Organization: UNG - University of Nova Gorica
UDC: 539.1
COBISS: 166306563 Link will open in a new window
ISSN: 1824-8039
Views: 27
Downloads: 0
Average score: 0 (0 votes)
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Other data

Type (COBISS): Not categorized
Pages: str. 1-6
Chronology: 2022
DOI: 10.22323/1.395.0384
ID: 20010300