| Naslov: | Mass composition of Telescope Array's surface detectors events using deep learning |
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| Avtorji: | ID Kharuk, I. (Avtor)
ID Lundquist, Jon Paul (Avtor), et al. |
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| Datoteke: |  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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| 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: | 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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| Ključne besede: | Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks |
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| Status publikacije: | Objavljeno |
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| Leto izida: | 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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| Datum objave v RUNG: | 29.09.2023 |
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| Število ogledov: | 1678 |
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| Število prenosov: | 5 |
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| Metapodatki: |    |
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