PoS - Proceedings of Science
Volume 410 - The 5th International Workshop on Deep Learning in Computational Physics (DLCP2021) - Regular papers
Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Machine Learning Methods
M.¬†Vasyutina*, L.¬†Sveshnikova, I.I.¬†Astapov, P.A.¬†Bezyazeekov, M.¬†Blank, E.A.¬†Bonvech, A.N.¬†Borodin, M.¬†Brueckner, N.M.¬†Budnev, A.V.¬†Bulan, D.V.¬†Chernov, A.¬†Chiavassa, A.N.¬†Dyachok, A.R.¬†Gafarov, A.Y.¬†Garmash, V.M.¬†Grebenyuk, O.A.¬†Gress, T.I.¬†Gress, A.A.¬†Grinyuk, O.G.¬†Grishin, D.¬†Horns, A.L.¬†Ivanova, N.N.¬†Kalmykov, V.V.¬†Kindin, S.N.¬†Kiryuhin, R.P.¬†Kokoulin, K.G.¬†Kompaniets, E.E.¬†Korosteleva, V.A.¬†Kozhin, E.A.¬†Kravchenko, A.P.¬†Kryukov, L.A.¬†Kuzmichev, A.A.¬†Lagutin, M.V.¬†Lavrova, Y.¬†Lemeshev, B.K.¬†Lubsandorzhiev, N.B.¬†Lubsandorzhiev, A.D.¬†Lukanov, D.¬†Lukyantsev, R.R.¬†Mirgazov, R.¬†Mirzoyan, R.D.¬†Monkhoev, E.A.¬†Osipova, A.L.¬†Pakhorukov, L.A.¬†Panasenko, A.¬†Pan, L.V.¬†Pankov, A.D.¬†Panov, A.A.¬†Petrukhin, D.A.¬†Podgrudkov, V.A.¬†Poleschuk, M.¬†Popesku, E.G.¬†Popova, A.¬†Porelli, E.B.¬†Postnikov, V.V.¬†Prosin, V.S.¬†Ptuskin, A.A.¬†Pushnin, R.I.R.¬†ūĚĎó, A.¬†Razumov, E.¬†Rjabov, G.I.¬†Rubtsov, Y.I.¬†Sagan, V.S.¬†Samoliga, A.Y.¬†Sidorenkov, A.A.¬†Silaev, A.A.¬†Silaev jr, A.V.¬†Skurikhin, M.¬†Slunecka, A.V.¬†Sokolov, Y.¬†Suvorkin, V.A.¬†Tabolenko, A.B.¬†Tanaev, B.A.¬†Tarashansky, M.¬†Ternovoy, L.G.¬†Tkachev, M.¬†Tluczykont, N.¬†Ushakov, A.¬†Vaidyanathan, P.A.¬†Volchugov, N.V.¬†Volkov, D.¬†Voronin, R.¬†Wischnewski, I.I.¬†Yashin, A.V.¬†Zagorodnikov and D.P.¬†Zhurovet al. (click to show)
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Pre-published on: December 01, 2021
Published on: January 12, 2022
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 \(km^2\) 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 ‚ÄėScaled Hillas Parameters‚Äô 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.
DOI: https://doi.org/10.22323/1.410.0008
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