A method for multimodal analysis of TAIGA experiment data using essential features
Abstract
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 achieved by multi-stage processing of experimental data, during which noise associated with measurements is suppressed and the dimensionality of the input data is reduced. In this paper, we propose a new method based on the use of neural networks such as autoencoders to extract essential features. The special value of the proposed approach lies in the possibility of its application to the analysis of multimodal data received simultaneously from several installations. We will apply this approach to a multimodal data (MMD) of the experiment TAIGA. Currently, the analysis of the MMD is carried out independently for each installation separately. Therefore, the development of methods for the joint analysis of MMD from TAIGA-type installations is an urgent task in cosmic ray physics and gamma-ray astronomy. Based on Monte Carlo simulation, it is shown that the proposed method allows for effective MMD analysis. It can also be used for MMD analysis at other experimental complexes.
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BibTeX
@misc{kryukov2026method,
title = {{A method for multimodal analysis of TAIGA experiment data using essential features}},
author = {Alexander Kryukov and Julia Dubenskaya and Elena Fedotova and Elizaveta Gres and Stanislav Polyakov and Eugene Postnikov and Alexander Razumov and Pavel Volchugov and Dmitry Zhurov},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08985},
eprint = {2610.08985},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08985},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A method for multimodal analysis of TAIGA experiment data using essential features
AU - Alexander Kryukov
AU - Julia Dubenskaya
AU - Elena Fedotova
AU - Elizaveta Gres
AU - Stanislav Polyakov
AU - Eugene Postnikov
AU - Alexander Razumov
AU - Pavel Volchugov
AU - Dmitry Zhurov
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.08985
UR - https://arxiv.org/abs/2610.08985
AB - 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 achieved by multi-stage processing of experimental data, during which noise associated with measurements is suppressed and the dimensionality of the input data is reduced. In this paper, we propose a new method based on the use of neural networks such as autoencoders to extract essential features. The special value of the proposed approach lies in the possibility of its application to the analysis of multimodal data received simultaneously from several installations. We will apply this approach to a multimodal data (MMD) of the experiment TAIGA. Currently, the analysis of the MMD is carried out independently for each installation separately. Therefore, the development of methods for the joint analysis of MMD from TAIGA-type installations is an urgent task in cosmic ray physics and gamma-ray astronomy. Based on Monte Carlo simulation, it is shown that the proposed method allows for effective MMD analysis. It can also be used for MMD analysis at other experimental complexes.
ER -