Alexander Kryukov
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Alexander Kryukov has a total of 832 co-authors. Here are the top co-authors by number of shared publications:
- Andrey Demichev
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Leninskie Gory, 1, Moscow, 119991, Russia
13 shared publications - Stanislav P. Polyakov
Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics 119991 Leninskie gory 1 bld. 2 Moscow Russia
10 shared publications A. P. Demichev
Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow, Russia
8 shared publicationsD. P. Zhurov
Research Institute of Applied Physics, Irkutsk State University, Irkutsk, Russia
7 shared publicationsE. O. Gres
Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow, Russia
7 shared publications- Evgeny Postnikov
Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow, Russia
7 shared publications
Latest Publications
- Open Access
- 17 September 2026
- Published by Springer Nature in The European Physical Journal C
- Vol. 86 (9) , 1066
Publisher Correction: Sensitivity of the Hyper-Kamiokande experiment to neutrino oscillation parameters using accelerator neutrinos
- 17 September 2026
- Published by Springer Nature in The European Physical Journal C
- Vol. 86 (9) , 1066
Open AccessPublisher Correction: Sensitivity of the Hyper-Kamiokande experiment to neutrino oscillation parameters using accelerator neutrinos
- 20 July 2026
- Published by Pleiades Publishing Ltd in Physics of Particles and Nuclei
- Vol. 57 (4) , 722-724
Application of Normalizing Flows for Detecting Rare Events in Gamma-Ray Astronomy
The goal of this work is to develop a new method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment.
- 20 July 2026
- Published by Pleiades Publishing Ltd in Physics of Particles and Nuclei
- Vol. 57 (4) , 722-724
Application of Normalizing Flows for Detecting Rare Events in Gamma-Ray Astronomy
The goal of this work is to develop a new method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment.
- Top Cited PapersOpen Access
- 23 February 2026
- Published by Springer Nature in The European Physical Journal C
- Vol. 86 (2) , 170
Sensitivity of the Hyper-Kamiokande experiment to neutrino oscillation parameters using accelerator neutrinos
This paper presents the expected sensitivity to the neutrino oscillation parameters of the Hyper-Kamiokande long-baseline program. The Hyper-Kamiokande experiment, currently under construction in Japan, will measure the oscillations of accelerator-produced neutrinos with thousands of selected events per sample: this corresponds to an increase of statistics of a factor 25–100 with respect to recent results from the currently-running long-baseline neutrino oscillation experiment in Japan, T2K. In the most favorable scenario we will achieve the discovery of Charge-Parity (CP) violation in neutrino oscillation at $$5\sigma $$ C.L. in less than 3 years. With 10 years of data-taking, and assuming a neutrino : antineutrino beam running ratio of 1:3, a CP violation discovery at $$5\sigma $$ C.L. is possible for more than 60% of the actual values of the CP-violating phase, $$\delta _{CP}.$$ Moreover, we will measure $$\delta _{CP}$$ with a precision ranging from 20 $$^{\circ },$$ in the case of maximal CP violation, to 6 $$^{\circ },$$ in the case of CP conservation. We aim to achieve a 0.5% resolution on the $$\Delta m^2_{32}$$ parameter, and a resolution between 3% and 0.5% on the $$\sin ^2\theta _{23}$$ parameter, depending on its true value. These results are obtained by extending the analysis methods of T2K with dedicated tuning to take into account the Hyper-Kamiokande design: the larger far detector, the more powerful beam, the upgraded near detector ND280, and the planned additional Intermediate Water Cherenkov Detector.- 23 February 2026
- Published by Springer Nature in The European Physical Journal C
- Vol. 86 (2) , 170
Top Cited PapersOpen AccessSensitivity of the Hyper-Kamiokande experiment to neutrino oscillation parameters using accelerator neutrinos
This paper presents the expected sensitivity to the neutrino oscillation parameters of the Hyper-Kamiokande long-baseline program. The Hyper-Kamiokande experiment, currently under construction in Japan, will measure the oscillations of accelerator-produced neutrinos with thousands of selected events per sample: this corresponds to an increase of statistics of a factor 25–100 with respect to recent results from the currently-running long-baseline neutrino oscillation experiment in Japan, T2K. In the most favorable scenario we will achieve the discovery of Charge-Parity (CP) violation in neutrino oscillation at $$5\sigma $$ C.L. in less than 3 years. With 10 years of data-taking, and assuming a neutrino : antineutrino beam running ratio of 1:3, a CP violation discovery at $$5\sigma $$ C.L. is possible for more than 60% of the actual values of the CP-violating phase, $$\delta _{CP}.$$ Moreover, we will measure $$\delta _{CP}$$ with a precision ranging from 20 $$^{\circ },$$ in the case of maximal CP violation, to 6 $$^{\circ },$$ in the case of CP conservation. We aim to achieve a 0.5% resolution on the $$\Delta m^2_{32}$$ parameter, and a resolution between 3% and 0.5% on the $$\sin ^2\theta _{23}$$ parameter, depending on its true value. These results are obtained by extending the analysis methods of T2K with dedicated tuning to take into account the Hyper-Kamiokande design: the larger far detector, the more powerful beam, the upgraded near detector ND280, and the planned additional Intermediate Water Cherenkov Detector. - 1 December 2025
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 80 (S3) , S1062-S1067
Capability of Using the Normalizing Flows for Extraction Rare Gamma Events in the TAIGA Experiment
The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in
- 1 December 2025
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 80 (S3) , S1062-S1067
Capability of Using the Normalizing Flows for Extraction Rare Gamma Events in the TAIGA Experiment
The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in
- Open Access
Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment
The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.
Open AccessCapability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment
The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.
- Open Access
Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks
The direction of extensive air showers can be used to determine the source of gamma quanta and plays an important role in estimating the energy of the primary particle. The data from an array of non-imaging Cherenkov detector stations HiSCORE in the TAIGA experiment registering the number of photoelectrons and detection time can be used to estimate the shower direction with high accuracy. In this work, we use artificial neural networks trained on Monte Carlo-simulated TAIGA HiSCORE data for gamma quanta to obtain shower direction estimates. The neural networks are multilayer perceptrons with skip connections using partial data from several HiSCORE stations as inputs; composite estimates are derived from multiple individual estimates by the neural networks. We apply a two-stage algorithm in which the direction estimates obtained in the first stage are used to transform the input data and refine the estimates. The mean error of the final estimates is less than 0.25 degrees. The approach will be used for multimodal analysis of the data from several types of detectors used in the TAIGA experiment.
Open AccessEvaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks
The direction of extensive air showers can be used to determine the source of gamma quanta and plays an important role in estimating the energy of the primary particle. The data from an array of non-imaging Cherenkov detector stations HiSCORE in the TAIGA experiment registering the number of photoelectrons and detection time can be used to estimate the shower direction with high accuracy. In this work, we use artificial neural networks trained on Monte Carlo-simulated TAIGA HiSCORE data for gamma quanta to obtain shower direction estimates. The neural networks are multilayer perceptrons with skip connections using partial data from several HiSCORE stations as inputs; composite estimates are derived from multiple individual estimates by the neural networks. We apply a two-stage algorithm in which the direction estimates obtained in the first stage are used to transform the input data and refine the estimates. The mean error of the final estimates is less than 0.25 degrees. The approach will be used for multimodal analysis of the data from several types of detectors used in the TAIGA experiment.
- Open Access
Machine Learning in Gamma Astronomy
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.
Open AccessMachine Learning in Gamma Astronomy
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.
- 1 December 2024
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 79 (S2) , S690-S699
Machine Learning in Gamma Astronomy
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenko
- 1 December 2024
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 79 (S2) , S690-S699
Machine Learning in Gamma Astronomy
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenko
- 1 December 2024
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 79 (S2) , S724-S730
Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connected Neural Networks
The TAIGA-HiSCORE setup is a wide-angle Cherenkov detector array for recording extensive air showers (EASs). The array comprises over 120 stations located
- 1 December 2024
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 79 (S2) , S724-S730
Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connected Neural Networks
The TAIGA-HiSCORE setup is a wide-angle Cherenkov detector array for recording extensive air showers (EASs). The array comprises over 120 stations located
- 1 December 2023
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 78 (S1) , S37-S44
The Use of Conditional Variational Autoencoders for Simulation of EAS Images from IACTs
Imaging atmospheric Cherenkov telescopes are used to record images of extensive area showers caused by high-energy particles colliding with the upper atmos
- 1 December 2023
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 78 (S1) , S37-S44
The Use of Conditional Variational Autoencoders for Simulation of EAS Images from IACTs
Imaging atmospheric Cherenkov telescopes are used to record images of extensive area showers caused by high-energy particles colliding with the upper atmos