论文标题
卷积神经网络在Taiga-Hiscore实验中的数据分析应用
Application of convolutional neural networks for data analysis in TAIGA-HiSCORE experiment
论文作者
论文摘要
TAIGA实验复合物是一种杂种观测值，用于从10 TEV到几个EEV的高能伽马射线天文学。该综合体包括Taigaiact，Taiga-Hiscore和其他许多装置。 Taiga-Hiscore设施是一组广角同步站，可检测散落在大面积的Cherenkov辐射。 Taiga-Hiscore数据提供了重建淋浴特性的机会，例如淋浴能，到达方向和轴坐标。这项工作的主要思想是应用卷积神经网络分析HISCORE事件，将其视为图像。 HISCORE站记录的注册时间和事件幅度的分布用作输入数据。本文提出了使用卷积神经网络来确定空气阵雨的特征的结果。结果表明，即使是卷积神经网络的简单模型也提供了恢复与传统方法相当的EAS参数的准确性。提出了在实际实验中获得的空气淋浴参数重建的初步结果，并提出了它们与传统分析结果的比较。
The TAIGA experimental complex is a hybrid observatory for high-energy gamma-ray astronomy in the range from 10 TeV to several EeV. The complex consists of such installations as TAIGA- IACT, TAIGA-HiSCORE and a number of others. The TAIGA-HiSCORE facility is a set of wide-angle synchronized stations that detect Cherenkov radiation scattered over a large area. TAIGA-HiSCORE data provides an opportunity to reconstruct shower characteristics, such as shower energy, direction of arrival, and axis coordinates. The main idea of the work is to apply convolutional neural networks to analyze HiSCORE events, considering them as images. The distribution of registration times and amplitudes of events recorded by HiSCORE stations is used as input data. The paper presents the results of using convolutional neural networks to determine the characteristics of air showers. It is shown that even a simple model of convolutional neural network provides the accuracy of recovering EAS parameters comparable to the traditional method. Preliminary results of air shower parameters reconstruction obtained in a real experiment and their comparison with the results of traditional analysis are presented.
