发表机构
Lomonosov Moscow State University; Irkutsk State University（莫斯科国立罗蒙诺索夫大学; 伊尔库茨克国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于自编码器提取关键特征的神经网络方法，用于TAIGA实验多模态数据联合分析，蒙特卡洛模拟验证有效，可推广至其他实验设施。
AI 中文摘要
处理和分析物理实验数据的目的是获取关于所研究现象的具有物理意义的信息。这一目标通过多阶段的数据处理来实现，在此过程中，与测量相关的噪声被抑制，输入数据的维度被降低。本文提出了一种基于自编码器等神经网络来提取关键特征的新方法。该方法特别的价值在于其能够应用于分析同时从多个装置接收到的多模态数据。我们将此方法应用于TAIGA实验的多模态数据（MMD）。目前，MMD的分析是分别针对每个装置独立进行的。因此，开发针对TAIGA型装置MMD联合分析的方法，是宇宙射线物理和伽马射线天文学领域的一项紧迫任务。基于蒙特卡洛模拟，我们证明了所提出的方法能够有效地进行MMD分析。该方法也可用于其他实验综合体的MMD分析。
英文摘要
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.
