Yana Zhezher (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

Mass composition anisotropy is predicted by a number of theories describing sources of ultra-high-energy cosmic rays. Event-by-event determination of a type of a primary cosmic-ray particle is impossible due to large shower-to-shower fluctuations, and the mass composition usually is obtained by averaging over some composition-sensitive observable determined independently for each extensive air shower (EAS) over a large number of events. In the present study we propose to employ the observable ξ used in the TA mass composition analysis for the mass composition anisotropy analysis. The ξ variable is determined with the use of Boosted Decision Trees (BDT) technique trained with the Monte-Carlo sets, and the ξ value is assigned for each event, where ξ=1 corresponds to an event initiated by the primary iron nuclei and ξ=−1 corresponds to a proton event. Use of ξ distributions obtained for the Monte-Carlo sets allows us to separate proton and iron candidate events from a data set with some given accuracy and study its distributions over the observed part of the sky. Results for the TA SD 11-year data set mass composition anisotropy will be presented.

Keywords

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

Data

Language: English
Year of publishing:
Typology: 1.08 - Published Scientific Conference Contribution
Organization: UNG - University of Nova Gorica
UDC: 539.1
COBISS: 166984451 Link will open in a new window
ISSN: 1824-8039
Views: 22
Downloads: 0
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Other data

Type (COBISS): Not categorized
Pages: str. 1-9
Chronology: 2022
DOI: 10.22323/1.395.0299
ID: 20033959