A method for multimodal analysis of TAIGA experiment data using essential features

תקציר מקורי באנגליתarXiv:2610.08985v1 Announce Type: cross 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