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1.
Mass composition anisotropy with the Telescope Array Surface Detector data
Y. Zhezher, Jon Paul Lundquist, 2022, objavljeni znanstveni prispevek na konferenci

Opis: 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.
Ključne besede: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, anisotropy, machine learning, boosted decision tree
Objavljeno v RUNG: 04.10.2023; Ogledov: 551; Prenosov: 5
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2.
Cosmic-ray mass composition with the TA SD 12-year data
Y. Zhezher, Jon Paul Lundquist, 2022, objavljeni znanstveni prispevek na konferenci

Opis: Telescope Array (TA) is the largest ultra-high-energy cosmic-ray (UHECR) observatory in the Northern Hemisphere. It is dedicated to detect extensive air showers (EAS) in hybrid mode, both by measuring the shower’s longitudinal profile with fluorescence telescopes and their particle footprint on the ground from the surface detector (SD) array. While fluorescence telescopes can measure the most composition-sensitive characteristic of EAS, the depth of the shower maximum (\xmax), they also have the drawback of small duty cycle. This work aims to study the UHECR composition based solely on the surface detector data. For this task, a set of composition-sensitive observables obtained from the SD data is used in a machine-learning method -- the Boosted Decision Trees. We will present the results of the UHECR mass composition based on the 12-year data from the TA SD using this technique, and we will discuss of the possible systematics imposed by the hadronic interaction models.
Ključne besede: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, machine learning, boosted decision tree
Objavljeno v RUNG: 04.10.2023; Ogledov: 538; Prenosov: 7
.pdf Celotno besedilo (763,42 KB)
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