Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array

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

doi:10.1088/1748-0221/17/05/P05023

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000478779 1001_ $$0I.I.Astapov.1$$aAstapov, I.$$b0
000478779 245__ $$aIdentification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array
000478779 260__ $$aLondon$$bInst. of Physics$$c2022
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000478779 520__ $$aThe 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%.
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000478779 650_7 $$2INSPIRE$$ashowers, atmosphere
000478779 650_7 $$2INSPIRE$$aenergy, high
000478779 650_7 $$2INSPIRE$$ascintillation counter, liquid
000478779 650_7 $$2INSPIRE$$aphoton, interaction
000478779 650_7 $$2INSPIRE$$aair
000478779 650_7 $$2INSPIRE$$aefficiency
000478779 650_7 $$2INSPIRE$$aneural network
000478779 650_7 $$2INSPIRE$$aGEANT
000478779 650_7 $$2INSPIRE$$aparticle identification
000478779 650_7 $$2INSPIRE$$aelectromagnetic
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000478779 650_7 $$2INSPIRE$$aperformance
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000478779 650_7 $$2autogen$$aData processing methods
000478779 650_7 $$2autogen$$aDetector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc)
000478779 650_7 $$2autogen$$aParticle identification methods
000478779 650_7 $$2autogen$$aScintillators, scintillation and light emission processes (solid, gas and liquid scintillators)
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