Generation of Grid Surface Detector Data in the Telescope Array Experiment Using Neural Networks

IF 0.4 4区 物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY
R. R. Fitagdinov, I. V. Kharuk
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引用次数: 0

Abstract

In this article, we talk about generating data obtained in the Telescope Array experiment. For this we are using Wasserstein’s generative adversarial networks. Wasserstein’s generative adversarial networks were trained on data obtained using the Monte Carlo method. To improve the quality of the generation, we add the loss function for the generator, which is based on the physics of the process of spreading an extensive air shower. In the future, this network can be used to search for anomalies and for faster data generation, compared with algorithms based on the Monte Carlo method.

Abstract Image

望远镜阵列实验中网格表面探测器数据的神经网络生成
在本文中,我们讨论了如何生成望远镜阵列实验中获得的数据。为此,我们使用了沃瑟斯坦的生成对抗网络。Wasserstein的生成对抗网络是在使用蒙特卡罗方法获得的数据上训练的。为了提高发电质量,我们增加了发电机的损失函数,这是基于大面积风淋室扩散过程的物理特性。在未来,与基于蒙特卡罗方法的算法相比,该网络可用于搜索异常和更快的数据生成。
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来源期刊
Moscow University Physics Bulletin
Moscow University Physics Bulletin PHYSICS, MULTIDISCIPLINARY-
CiteScore
0.70
自引率
0.00%
发文量
129
审稿时长
6-12 weeks
期刊介绍: Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.
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