arXiv：cs.LG· Alexander Kryukov, Julia Dubenskaya, Elena Fedotova, Elizaveta Gres, Stanislav Polyakov, Eugene Postnikov, Alexander Razumov, Pavel Volchugov, Dmitry Zhurov·· 4 小时前AI 评分24
基于自编码器提取本质特征的多模态 TAIGA 实验数据分析方法
A method for multimodal analysis of TAIGA experiment data using essential features
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研究人员提出一种基于自编码器等神经网络提取本质特征的方法，可对来自多个装置的 TAIGA 实验多模态数据进行联合分析，而目前这类分析仍按单个装置独立进行。基于蒙特卡罗模拟显示，该方法能有效完成多模态数据分析，也可用于其他实验复合体的多模态分析。
正文
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.
| Subjects: | Instrumentation and Methods for Astrophysics (astro-ph.IM); High Energy Astrophysical Phenomena (astro-ph.HE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.08985 [astro-ph.IM] |
| (or arXiv:2610.08985v1 [astro-ph.IM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08985 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alexander Kryukov [view email]
[v1]
Tue, 6 Oct 2026 18:44:52 UTC (728 KB)
来源：arXiv：cs.LG · arxiv.org