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

Standard

Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array. / the TAIGA Collaboration.

In: Journal of Instrumentation, Vol. 17, No. 5, P05023, 01.05.2022.

Research output: Contribution to journal › Article › peer-review

Harvard

the TAIGA Collaboration 2022, 'Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array', Journal of Instrumentation, vol. 17, no. 5, P05023. https://doi.org/10.1088/1748-0221/17/05/P05023

APA

the TAIGA Collaboration (2022). Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array. Journal of Instrumentation, 17(5), [P05023]. https://doi.org/10.1088/1748-0221/17/05/P05023

Vancouver

the TAIGA Collaboration. Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array. Journal of Instrumentation. 2022 May 1;17(5):P05023. doi: 10.1088/1748-0221/17/05/P05023

Author

the TAIGA Collaboration. / Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array. In: Journal of Instrumentation. 2022 ; Vol. 17, No. 5.

BibTeX

@article{7282718e306a49ffb5f85ba88d6aeec8,

title = "Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array",

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%. ",

keywords = "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)",

author = "{the TAIGA Collaboration} and I. Astapov and P. Bezyazeekov and M. Blank and E. Bonvech and A. Borodin and M. Brueckner and N. Budnev and A. Bulan and D. Chernov and A. Chiavassa and A. Dyachok and A. Gafarov and A. Garmash and V. Grebenyuk and E. Gress and O. Gress and T. Gress and A. Grinyuk and O. Grishin and D. Horns and A. Igoshin and M. Ilyushin and Ivanova, {A. D.} and Ivanova, {A. L.} and N. Kalmykov and V. Kindin and S. Kiryuhin and R. Kokoulin and K. Kompaniets and E. Korosteleva and V. Kozhin and E. Kravchenko and A. Kryukov and A. Kuotb and L. Kuzmichev and A. Lagutin and M. Lavrova and Y. Lemeshev and B. Lubsandorzhiev and N. Lubsandorzhiev and A. Lukanov and D. Lukyantsev and S. Malakhov and R. Mirgazov and R. Mirzoyan and R. Monkhoev and E. Osipova and A. Sokolov and A. Vaidyanathan and V. Vorobyov",

note = "Publisher Copyright: {\textcopyright} 2022 IOP Publishing Ltd and Sissa Medialab",

year = "2022",

month = may,

day = "1",

doi = "10.1088/1748-0221/17/05/P05023",

language = "English",

volume = "17",

journal = "Journal of Instrumentation",

issn = "1748-0221",

publisher = "IOP Publishing Ltd.",

number = "5",

}

RIS

TY - JOUR

T1 - Identification of electromagnetic and hadronic EASs using neural network for TAIGA scintillation detector array

AU - the TAIGA Collaboration

AU - Astapov, I.

AU - Bezyazeekov, P.

AU - Blank, M.

AU - Bonvech, E.

AU - Borodin, A.

AU - Brueckner, M.

AU - Budnev, N.

AU - Bulan, A.

AU - Chernov, D.

AU - Chiavassa, A.

AU - Dyachok, A.

AU - Gafarov, A.

AU - Garmash, A.

AU - Grebenyuk, V.

AU - Gress, E.

AU - Gress, O.

AU - Gress, T.

AU - Grinyuk, A.

AU - Grishin, O.

AU - Horns, D.

AU - Igoshin, A.

AU - Ilyushin, M.

AU - Ivanova, A. D.

AU - Ivanova, A. L.

AU - Kalmykov, N.

AU - Kindin, V.

AU - Kiryuhin, S.

AU - Kokoulin, R.

AU - Kompaniets, K.

AU - Korosteleva, E.

AU - Kozhin, V.

AU - Kravchenko, E.

AU - Kryukov, A.

AU - Kuotb, A.

AU - Kuzmichev, L.

AU - Lagutin, A.

AU - Lavrova, M.

AU - Lemeshev, Y.

AU - Lubsandorzhiev, B.

AU - Lubsandorzhiev, N.

AU - Lukanov, A.

AU - Lukyantsev, D.

AU - Malakhov, S.

AU - Mirgazov, R.

AU - Mirzoyan, R.

AU - Monkhoev, R.

AU - Osipova, E.

AU - Sokolov, A.

AU - Vaidyanathan, A.

AU - Vorobyov, V.

PY - 2022/5/1

Y1 - 2022/5/1

N2 - 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%.

AB - 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%.

KW - Data processing methods

KW - Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc)

KW - Particle identification methods

KW - Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators)

UR - http://www.scopus.com/inward/record.url?scp=85130715126&partnerID=8YFLogxK

U2 - 10.1088/1748-0221/17/05/P05023

DO - 10.1088/1748-0221/17/05/P05023

M3 - Article

AN - SCOPUS:85130715126

VL - 17

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

IS - 5

M1 - P05023

ER -

ID: 36188263