| Title: | Mass composition of Telescope Array's surface detectors events using deep learning |
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| Authors: | ID Kharuk, I. (Author)
ID Lundquist, Jon Paul (Author), et al. |
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| Files: |  ICRC2021_384.pdf (788,87 KB)
MD5: BA78663A7EA8CACD24AB401D8122F062
 https://pos.sissa.it/395/384/
https://pos.sissa.it/395/384/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: | 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. |
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| Keywords: | Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks |
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| Publication status: | Published |
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| Year of publishing: | 2022 |
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| PID: | 20.500.12556/RUNG-8479  |
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| COBISS.SI-ID: | 166306563 |
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| DOI: | 10.22323/1.395.0384 |
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| NUK URN: | URN:SI:UNG:REP:N24WRR9E |
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| Publication date in RUNG: | 29.09.2023 |
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| Views: | 756 |
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| Downloads: | 4 |
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