First results of the tracking system calibration of the TAIGA-IACT telescope

Zhurov, D.; Гресс, О. А.; Sidorov, Denis; Астапов, И. И.; Bezyazeekov, P. A.; Boreyko, V.; Бородин, А.; Буднев, Н.; Brueckner, Martin; Чиавасса, А.; Dyachok, A.; Fedorov, Oleg; Gafarov, A.; Garmash, A.; Gorbunov, N.; Grebenyuk, V.; Гресс, Т.; Гришин, О.; Grinyuk, A.; Horns, D.; Иванова, А.; Калмыков, Н. Н.; Kazarina, Y.; Киндин, В. В.; Kirilenko, P.; Kiryuhin, S.; Кокоулин, Р. П.; Компаниец, К. Г.; Коростелева, Е. Е.; Kozhin, V.; Кравченко, Е. А.; Куннас, М.; Kuzmichev, L. A.; Lemeshev, Yu.; Lenok, Vladimir; Лубсандоржиев, Б.; Лубсандоржиев, Н.; Миргазов, Р. Р.; Mirzoyan, R.; Монхоев, Р.; Nachtigall, R.; Osipova, E.; Пахоруков, А.; Panasyuk, M. I.; Pankov, L.; Петрухин, А. А.; Poleschuk, V.; Popescu, M.; Popova, E.; Порелли, А.; Постников, Е.; Prosin, V. V.; Птускин, В. С.; Rjabov, E.V.; Рубцов, Г.; Пушнин, А.; Sagan, Y.; Сабиров, Б. М.; Самолига, В.; Semeney, Yu.; Силаев, А.; Sidorenkov, A.; Скурихин, А. В.; Slunecka, V.; Sokolov, A.; Spiering, Christian; Свешникова, Л.; Таболенко, В. А.; Tarashansky, B.; Tkachenko, A.; Ткачев, Л.; Tluczykont, M.; Wischnewski, R.; Загородников, А.; Зурбанов, В. Л.; Yashin, I. I.

doi:10.1088/1742-6596/1181/1/012045

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…As an experimental sample for the analysis, we have chosen the data detected with the IACT01 in TAIGA experiment during the October, November and the first part of December 2019 year at pointing to the source Crab Nebula (Crab -'standard candle source' in TeV gamma astronomy), published in [8]. In the TAIGA experiment a 'wobbling' tracking system of the telescope was realized (see details in [9]): Experimental sample consists of 'On' and 'Off' samples. The first one corresponds to the case when telescope follows the source in the sky, the second one, when telescope follows a background point in the sky.…”

Section: Experimental Samples and Monte-carlo Training Samplesmentioning

confidence: 99%

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

Vasyutina¹

2021

Proceedings of the 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021)

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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 '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.

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Section: Experimental Samples and Monte-carlo Training Samplesmentioning

confidence: 99%

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

Vasyutina¹

2021

Proceedings of the 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021)

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“…To determine the telescope position by the CCD camera a transformation of the telescope camera center to the CCD sky region is determined. It takes into account the possibility of small displacements of the CCD camera direction concerning the telescope optical axis and the possible dependence on the telescope altitude [11]. The parameters of the transformation are determined experimentally using bright stars and the calibration screen unrolled in the telescope camera.…”

Section: Transformation From the Ccd To Telescope Cameramentioning

confidence: 99%

TAIGA-IACT pointing control and monitoring software status

Zhurov¹,

Greß²,

Lukyantsev³

et al. 2021

Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)

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“…Using the residuals, the model is fitted. [25]. The CCD-camera transformation calibration starts with measurements of the reference LED positions.…”

Section: Event Reconstructionmentioning

confidence: 99%