2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
Model for synchronizer of marked pairs in fork-join network
S. V. Vyshenski, P. V. Grigoriev, Yu. Yu. Dubenskaya
We introduce a model for synchronizer of marked pairs, which is a node for joining results of parallel processing in two-branch fork-join queueing network. A distribution for numbe…
Ideal synchronizer for marked pairs in fork-join network
S. V. Vyshenski, P. V. Grigoriev, Yu. Yu. Dubenskaya
We introduce a new functional element (synchronizer for marked pairs) meant to join results of parallel processing in two-branch fork-join queueing network. Approximations for dist…