24-28 February 2020
Budker Institute of Nuclear Physics
Asia/Novosibirsk timezone

Identification of ultrahigh energy extensive air showers with Taiga-Muon installation

27 Feb 2020, 17:50
Contributed Oral Instrumentation for Astroparticle and Neutrino physics Particle Identification


Mr Arun Vaidyanathan (PhD Student)


The TAIGA astroparticle observatory is under the construction at Tunka valley close to the Baikal Lake. Up to now it consists of 2 imaging air Cherenkov telescopes, about 100 wide-angle optical detectors, and 19 stations with 342 scintillation detectors. In 2019, the existing system of scintillation detector stations was extended with 3 stations of the new type Taiga-Muon counters. Each station contains 16 counters, with 8 surface and 8 underground counters. The counter and station positioning has been studied using specially developed Monte Carlo simulation program based on of CORSIKA and GEANT4 software packages. This simulation study is concentrated on the ultrahigh energy extensive air showers (EAS) induced by gamma-quanta or proton in the range from 1 PeV to 10 PeV and zenith angle ranging 00 - 450. The simulation results are analyzed with the help of neural network. For this work, a set of air showers was created by CORSIKA. The list of useful secondary particles at the ground level is produced using the COAST library package. The interaction of secondary particles with the soil and detectors was simulated with GEANT4 package. It is known, that the lateral distributions of particle density in gamma-quanta and proton EAS are different at the ground level. Also the density of muons is different. To use both these characteristics for separation of gamma-quanta from proton we suggest using a neural network. The method called binary cross entropy was studied. Amplitudes in surface and underground counters of each station were given as input data. The air shower having energy ranging 2.25 - 3.5 PeV shows more than 90% of identification efficiency for proton by keeping identification efficiency of gamma around 50%.

Primary author

Mr Arun Vaidyanathan (PhD Student)


Dr Evgeniy Kravchenko (BINP/NSU)

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