| Naslov: | Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning |
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| Avtorji: | ID Kalashev, O. (Avtor)
ID Lundquist, Jon Paul (Avtor), et al. |
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| Datoteke: |  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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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Delo ni kategorizirano |
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| Tipologija: | 1.08 - Objavljeni znanstveni prispevek na konferenci |
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| Organizacija: | UNG - Univerza v Novi Gorici
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| Opis: | 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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| Ključne besede: | 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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| Status publikacije: | Objavljeno |
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| Leto izida: | 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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| Datum objave v RUNG: | 04.10.2023 |
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| Število ogledov: | 1559 |
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| Število prenosov: | 8 |
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| Metapodatki: |    |
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| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
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