Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array. (1st May 2022) - ,; Astapov, I.; Bezyazeekov, P.; Blank, M.; Bonvech, E.; Borodin, A.; Brueckner, M.; Budnev, N.; Bulan, A.; Chernov, D.; Chiavassa, A.; Dyachok, A.; et al.
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%.