| Title: | Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning |
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| Authors: | ID Kalashev, O. (Author)
ID Lundquist, Jon Paul (Author), et al. |
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| Files: |  ICRC2021_252.pdf (1,10 MB)
MD5: 5167552A9E94058603FB45BB0F410D20
 https://pos.sissa.it/395/252/
https://pos.sissa.it/395/252/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: | A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure. |
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| Keywords: | Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, energy, arrival directions, reconstruction, machine learning, neural network |
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| Publication status: | Published |
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| Year of publishing: | 2022 |
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| PID: | 20.500.12556/RUNG-8533  |
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| COBISS.SI-ID: | 167027459 |
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| DOI: | 10.22323/1.395.0252 |
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| NUK URN: | URN:SI:UNG:REP:A0BAT5BZ |
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| Publication date in RUNG: | 04.10.2023 |
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| Views: | 1557 |
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| Downloads: | 8 |
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