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Application of Convolutional Neural Networks in Neutrino Physics

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00335963" target="_blank" >RIV/68407700:21340/19:00335963 - isvavai.cz</a>

  • Result on the web

    <a href="http://gams.fjfi.cvut.cz/spms2019" target="_blank" >http://gams.fjfi.cvut.cz/spms2019</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Application of Convolutional Neural Networks in Neutrino Physics

  • Original language description

    The application of deep learning methods has in past years enabled new discoveries in many fields of study and neutrino physics is no exception. As a deep learning algorithm, convolutional neural networks (CNNs) show outstanding results in the domain of computer vision. Thus, they are being used as a particle classifier using visual image data reconstructed from various neutrino experiments. In this paper, we present a study of concepts of artificial neural networks (ANNs) as well as CNNs. Furthermore, we present the classification results of Monte Carlo simulated images from neutrino experiment DUNE.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of SPMS 2019 - Stochastic and Physical Monitoring Systems

  • ISBN

    978-80-01-06659-1

  • ISSN

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    85-91

  • Publisher name

    Česká technika - nakladatelství ČVUT

  • Place of publication

    Praha

  • Event location

    Dobřichovice

  • Event date

    Jun 20, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article