Standard

Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods. / the TAIGA Collaboration.

In: Proceedings of Science, Vol. 410, 008, 12.01.2022.

Research output: Contribution to journal › Conference article › peer-review

Harvard

the TAIGA Collaboration 2022, 'Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods', Proceedings of Science, vol. 410, 008. https://doi.org/10.22323/1.410.0008

APA

the TAIGA Collaboration (2022). Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods. Proceedings of Science, 410, [008]. https://doi.org/10.22323/1.410.0008

Vancouver

the TAIGA Collaboration. Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods. Proceedings of Science. 2022 Jan 12;410:008. doi: 10.22323/1.410.0008

Author

the TAIGA Collaboration. / Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods. In: Proceedings of Science. 2022 ; Vol. 410.

BibTeX

@article{f36e2d9dfccc4f32b4b58ce0180f7b28,
title = "Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods",
abstract = "In this paper we present the first attempt of adaptation the Random Forest (RF) machine learning algorithm to gamma/hadron separation in the TAIGA experiment (Tunka Advanced Instrument for cosmic ray physics and Gamma-ray Astronomy). The TAIGA experiment will include HiSCORE array with 120 wide-angle Cherenkov detectors on the area of 1 km2 and 5 Imaging Atmospheric Cherenkov Telescopes (IACT) on the same area. At the first stage of the analysis, only images obtained by one IACT were included in consideration. The training process occurs on samples of parameterized images obtained from Monte Carlo (MC) data for gammas and hadrons with a {\textquoteleft}Scaled Hillas Parameters{\textquoteright} standard technique. It was shown that the program effectively separates gamma-like showers, RF method does produce stable results and is robust with respect to input parameters and provides a simple control and setup of the procedure for extracting showers from gamma rays.",
author = "{the TAIGA Collaboration} and Maria Vasyutina and Lyubov Sveshnikova and Astapov, {I. I.} and Bezyazeekov, {P. A.} and M. Blank and Bonvech, {E. A.} and Borodin, {A. N.} and M. Brueckner and Budnev, {N. M.} and Bulan, {A. V.} and Chernov, {D. V.} and A. Chiavassa and Dyachok, {A. N.} and Gafarov, {A. R.} and Garmash, {A. Yu} and Grebenyuk, {V. M.} and Gress, {O. A.} and Gress, {T. I.} and Grinyuk, {A. A.} and Grishin, {O. G.} and D. Horns and Ivanova, {A. L.} and Kalmykov, {N. N.} and Kindin, {V. V.} and Kiryuhin, {S. N.} and Kokoulin, {R. P.} and Kompaniets, {K. G.} and Korosteleva, {E. E.} and Kozhin, {V. A.} and Kravchenko, {E. A.} and Kryukov, {A. P.} and Kuzmichev, {L. A.} and Lagutin, {A. A.} and Lavrova, {M. V.} and Yu Lemeshev and Lubsandorzhiev, {B. K.} and Lubsandorzhiev, {N. B.} and Lukanov, {A. D.} and D. Lukyantsev and Mirgazov, {R. R.} and R. Mirzoyan and Monkhoev, {R. D.} and Osipova, {E. A.} and Pakhorukov, {A. L.} and Panasenko, {L. A.} and A. Pan and Pankov, {L. V.} and Rubtsov, {G. I.} and Sokolov, {A. V.} and A. Vaidyanathan",
note = "Funding Information: The work was performed at the unique scientific installation {"}Astrophysical Complex of MSU-ISU{"}(agreement 13.UNU.21.0007). This work also is supported by the Russian Science Foundation grant N 19-72-20067. Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).; 5th International Workshop on Deep Learning in Computational Physics, DLCP 2021 ; Conference date: 28-06-2021 Through 29-06-2021",
year = "2022",
month = jan,
day = "12",
doi = "10.22323/1.410.0008",
language = "English",
volume = "410",
journal = "Proceedings of Science",
issn = "1824-8039",
publisher = "Sissa Medialab Srl",

}

RIS

TY - JOUR

T1 - Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods

AU - the TAIGA Collaboration

AU - Vasyutina, Maria

AU - Sveshnikova, Lyubov

AU - Astapov, I. I.

AU - Bezyazeekov, P. A.

AU - Blank, M.

AU - Bonvech, E. A.

AU - Borodin, A. N.

AU - Brueckner, M.

AU - Budnev, N. M.

AU - Bulan, A. V.

AU - Chernov, D. V.

AU - Chiavassa, A.

AU - Dyachok, A. N.

AU - Gafarov, A. R.

AU - Garmash, A. Yu

AU - Grebenyuk, V. M.

AU - Gress, O. A.

AU - Gress, T. I.

AU - Grinyuk, A. A.

AU - Grishin, O. G.

AU - Horns, D.

AU - Ivanova, A. L.

AU - Kalmykov, N. N.

AU - Kindin, V. V.

AU - Kiryuhin, S. N.

AU - Kokoulin, R. P.

AU - Kompaniets, K. G.

AU - Korosteleva, E. E.

AU - Kozhin, V. A.

AU - Kravchenko, E. A.

AU - Kryukov, A. P.

AU - Kuzmichev, L. A.

AU - Lagutin, A. A.

AU - Lavrova, M. V.

AU - Lemeshev, Yu

AU - Lubsandorzhiev, B. K.

AU - Lubsandorzhiev, N. B.

AU - Lukanov, A. D.

AU - Lukyantsev, D.

AU - Mirgazov, R. R.

AU - Mirzoyan, R.

AU - Monkhoev, R. D.

AU - Osipova, E. A.

AU - Pakhorukov, A. L.

AU - Panasenko, L. A.

AU - Pan, A.

AU - Pankov, L. V.

AU - Rubtsov, G. I.

AU - Sokolov, A. V.

AU - Vaidyanathan, A.

N1 - Funding Information: The work was performed at the unique scientific installation "Astrophysical Complex of MSU-ISU"(agreement 13.UNU.21.0007). This work also is supported by the Russian Science Foundation grant N 19-72-20067. Publisher Copyright: © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

PY - 2022/1/12

Y1 - 2022/1/12

N2 - In this paper we present the first attempt of adaptation the Random Forest (RF) machine learning algorithm to gamma/hadron separation in the TAIGA experiment (Tunka Advanced Instrument for cosmic ray physics and Gamma-ray Astronomy). The TAIGA experiment will include HiSCORE array with 120 wide-angle Cherenkov detectors on the area of 1 km2 and 5 Imaging Atmospheric Cherenkov Telescopes (IACT) on the same area. At the first stage of the analysis, only images obtained by one IACT were included in consideration. The training process occurs on samples of parameterized images obtained from Monte Carlo (MC) data for gammas and hadrons with a ‘Scaled Hillas Parameters’ standard technique. It was shown that the program effectively separates gamma-like showers, RF method does produce stable results and is robust with respect to input parameters and provides a simple control and setup of the procedure for extracting showers from gamma rays.

AB - In this paper we present the first attempt of adaptation the Random Forest (RF) machine learning algorithm to gamma/hadron separation in the TAIGA experiment (Tunka Advanced Instrument for cosmic ray physics and Gamma-ray Astronomy). The TAIGA experiment will include HiSCORE array with 120 wide-angle Cherenkov detectors on the area of 1 km2 and 5 Imaging Atmospheric Cherenkov Telescopes (IACT) on the same area. At the first stage of the analysis, only images obtained by one IACT were included in consideration. The training process occurs on samples of parameterized images obtained from Monte Carlo (MC) data for gammas and hadrons with a ‘Scaled Hillas Parameters’ standard technique. It was shown that the program effectively separates gamma-like showers, RF method does produce stable results and is robust with respect to input parameters and provides a simple control and setup of the procedure for extracting showers from gamma rays.

UR - http://www.scopus.com/inward/record.url?scp=85124072310&partnerID=8YFLogxK

U2 - 10.22323/1.410.0008

DO - 10.22323/1.410.0008

M3 - Conference article

AN - SCOPUS:85124072310

VL - 410

JO - Proceedings of Science

JF - Proceedings of Science

SN - 1824-8039

M1 - 008

T2 - 5th International Workshop on Deep Learning in Computational Physics, DLCP 2021

Y2 - 28 June 2021 through 29 June 2021

ER -

ID: 35427209