Journal Article

PUBDB-2022-02758

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array

Astapov, I. ; Bezyazeekov, P. ; Blank, M. ; Bonvech, E. ; Borodin, A. ; Brueckner, M.DESY* ; Budnev, N. ; Bulan, A. ; Chernov, D. ; Chiavassa, A. ; Dyachok, A. ; Gafarov, A. ; Garmash, A. ; Grebenyuk, V. ; Gress, E. ; Gress, O. ; Gress, T. ; Grinyuk, A. ; Grishin, O. ; Horns, D.Extern* ; Igoshin, A. ; Ilyushin, M. ; Ivanova, A. D. ; Ivanova, A. L. ; Kalmykov, N. ; Kindin, V. ; Kiryuhin, S. ; Kokoulin, R. ; Kompaniets, K. ; Korosteleva, E. ; Kozhin, V. ; Kravchenko, E. ; Kryukov, A. ; Kuotb, A. ; Kuzmichev, L. ; Lagutin, A. ; Lavrova, M. ; Lemeshev, Y. ; Lubsandorzhiev, B. ; Lubsandorzhiev, N. ; Lukanov, A. ; Lukyantsev, D. ; Malakhov, S. ; Mirgazov, R. ; Mirzoyan, R. ; Monkhoev, R. ; Osipova, E. ; Pakhorukov, A. ; Pan, A. ; Pankov, L. ; Panov, L. ; Petrukhin, A. ; Poddubnyi, I. ; Podgrudkov, D. ; Poleschuk, V. ; Ponomareva, V. ; Popesku, M. ; Popova, E. ; Porelli, A.DESY* ; Postnikov, E. ; Prosin, V. ; Ptuskin, V. ; Pushnin, A. ; Raikin, R. ; Rubtsov, G. ; Ryabov, E. ; Sagan, Y. ; Samoliga, V. ; Satyshev, I. ; Silaev, A. ; Silaev, A. (junior) ; Sidorenkov, A. ; Sinegovsky, S. ; Skurikhin, A. ; Sokolov, A. ; Sulakov, V. ; Sveshnikova, L. ; Tabolenko, V. ; Tanaev, A. ; Tarashchansky, B. ; Ternovoy, M. ; Tkachev, L. ; Tluczykont, M.Extern* ; Togoo, R. ; Ushakov, N. ; Vaidyanathan, A. (Corresponding author)Extern* ; Volchugov, P. ; Volkov, N. ; Vorobyov, V. ; Voronin, D. ; Wischnewski, R.DESY* ; Zagorodnikov, A. ; Zhaglova, A. ; Zhurov, D. ; Yashin, I. ; TAIGA Collaboration (Collaboration Author)

2022
Inst. of Physics London

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Please use a persistent id in citations: doi:10.1088/1748-0221/17/05/P05023

Abstract: The TAIGA experiment in Tunka valley is expanding the present scintillation detector array with new TAIGA-Muon detector stations. A simulation model was developed for optimization of the layout of the new stations and study of the identification performance of the array. The extensive air showers (EASs) were simulated with the CORSIKA simulation tool, and the detector response was simulated with the GEANT4 package. EASs induced by gamma quanta or protons in the energy range from 1 PeV to 10 PeV and the zenith angle range from 0° to 45°, are used for these studies. For the identification of high energy extensive air showers, a method based on a neural network was suggested. With this method, the proton identification efficiency is more than 90%, while the gamma identification efficiency not less than 50%.

Keyword(s): showers, atmosphere ; energy, high ; scintillation counter, liquid ; photon, interaction ; air ; efficiency ; neural network ; GEANT ; particle identification ; electromagnetic ; gas ; solids ; performance ; hadron ; Data processing methods ; Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc) ; Particle identification methods ; Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators)

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Contributing Institute(s):

1. Gammaastronomie (Z_GA)

Research Program(s):

1. 613 - Matter and Radiation from the Universe (POF4-613) (POF4-613)

Experiment(s):

1. Tunka Advanced Instrument for cosmic ray physics and Gamma Astronomy

Appears in the scientific report 2022

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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