7 citations · 22 across the 7 of their papers we have counts for
10 papers
Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
E. O. Gres, A. P. Kryukov, P. A. Volchigov +6
In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showe…
A method for multimodal analysis of TAIGA experiment data using essential features
Alexander Kryukov, Julia Dubenskaya, Elena Fedotova +6
The aim of processing and analyzing experimental data from physical experiments is to obtain physically significant information about the phenomenon under study. This goal is achie…
Analysis of the TAIGA-HiSCORE Data Using the Latent Space of Autoencoders
Yu. Yu. Dubenskaya, S. P. Polyakov, A. P. Kryukov +6
The aim of extensive air shower (EAS) analysis is to reconstruct the physical parameters of the primary particle that initiated the shower. The TAIGA experiment is a hybrid detecto…
Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks
Yu. Yu. Dubenskaya, A. P. Kryukov, E. O. Gres +5
Modern Imaging Atmospheric Cherenkov Telescopes (IACTs) generate a huge amount of data that must be classified automatically, ideally in real time. Currently, machine learning-base…
Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks
A. P. Kryukov, S. P. Polyakov, Yu. Yu. Dubenskaya +4
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 fr…
Gamma/hadron separation in the TAIGA experiment with neural network methods
E. O. Gres, A. P. Kryukov, P. A. Volchugov +5
In this work, the ability of rare VHE gamma ray selection with neural network methods is investigated in the case when cosmic radiation flux strongly prevails (ratio up to {10^4} o…
Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis
Julia Dubenskaya, Alexander Kryukov, Andrey Demichev +3
Generative adversarial networks are a promising tool for image generation in the astronomy domain. Of particular interest are conditional generative adversarial networks (cGANs), w…
Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes
Stanislav Polyakov, Alexander Kryukov, Andrey Demichev +3
High-energy particles hitting the upper atmosphere of the Earth produce extensive air showers that can be detected from the ground level using imaging atmospheric Cherenkov telesco…
Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods
E. O. Gres, A. P. Kryukov
Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical complex allow to observe high energy gamma radiation helping to study many astrophysical objects and processe…
The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks
Elizaveta Gres, and Alexander Kryukov
The imaging Cherenkov telescopes TAIGA-IACT, located in the Tunka valley of the republic Buryatia, accumulate a lot of data in a short period of time which must be efficiently and…