| Title: | Mass composition anisotropy with the Telescope Array Surface Detector data |
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| Authors: | ID Zhezher, Y. (Author)
ID Lundquist, Jon Paul (Author), et al. |
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| Files: |  ICRC2021_299.pdf (1,14 MB)
MD5: 0E101608760E103960110EA18E240713
 https://pos.sissa.it/395/299/
https://pos.sissa.it/395/299/pdf
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| Language: | English |
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| Work type: | Not categorized |
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| Typology: | 1.08 - Published Scientific Conference Contribution |
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| Organization: | UNG - University of Nova Gorica
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| 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. |
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| Keywords: | Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, anisotropy, machine learning, boosted decision tree |
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| Publication status: | Published |
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| Year of publishing: | 2022 |
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| PID: | 20.500.12556/RUNG-8525  |
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| COBISS.SI-ID: | 166984451 |
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| DOI: | 10.22323/1.395.0299 |
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| NUK URN: | URN:SI:UNG:REP:AQHSIAFL |
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| Publication date in RUNG: | 04.10.2023 |
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| Views: | 565 |
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| Downloads: | 5 |
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