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Deep Learning Techniques Applications for the ENDA Experiment Data Analysis

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Abstract

Abstract-Using of machine learning (ML) and deep learning (DL) techniques in data analysis becomes a mainstream today and is presented in papers of leading experiments. These modern methods allow sometimes to increase the accuracy of, for example, mass composition reconstruction significantly. In current work DL are applied for core location based on data of ENDA array CORSIKA + GEANT4 Monte Carlo simulations. ENDA (Electron Neutron Detector Array) is an extensive air shower (EAS) experiment based on idea of simultaneous recording of electromagnetic component of EAS and thermal neutrons produced by the hadron component of the shower. Results of the analysis are presented.

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Authors and Affiliations

  1. Institute for Nuclear Research of the Russian Academy of Sciences, Moscow, Russia

    O. Shchegolev

  2. Moscow Institute of Physics and Technology (National Research University), Moscow, Russia

    O. Shchegolev

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  1. O. Shchegolev
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Correspondence to O. Shchegolev.

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Shchegolev, O. Deep Learning Techniques Applications for the ENDA Experiment Data Analysis. Phys. Atom. Nuclei 84, 915–918 (2021). https://doi.org/10.1134/S1063778821130317

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  • DOI: https://doi.org/10.1134/S1063778821130317

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