4 citations · 4 across the 4 of their papers we have counts for
4 papers · 2 filters
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…