Application of Convolutional Neural Networks for Data Analysis in TAIGA-HiSCORE Experiment

被引：0
|
作者：
Kryukov, A. P. [1 ]
Vlaskina, A. A. [1 ,2 ]
Polyakov, S. P. [1 ]
Gres, E. O. [3 ]
Demichev, A. P. [1 ]
Dubenskaya, Yu. Yu. [1 ]
Zhurov, D. P. [3 ]
机构：
[1] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow 119991, Russia
[2] Lomonosov Moscow State Univ, Fac Phys, Moscow 119991, Russia
[3] Irkutsk State Univ, Appl Phys Inst, Irkutsk 664003, Russia
来源：
MOSCOW UNIVERSITY PHYSICS BULLETIN | 2023年 / 78卷 / SUPPL 1期
基金：
俄罗斯科学基金会;
关键词：
machine learning; convolutional neural networks; gamma astronomy; extensive air shower;
D O I：
10.3103/S0027134923070172
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
The Tunka Advanced Instrument for gamma-ray and cosmic ray Astrophysics (TAIGA) is a hybrid observatory for the detection of extensive air showers (EAS), produced by high-energy gamma rays and cosmic rays. The complex consists of such facilities as TAIGA-IACT, TAIGA-HiSCORE, and a variety of others. The goal of the study is to introduce a deep learning-based technique for EAS axis reconstruction. A convolutional neural network (CNN) model is proposed, while HiSCORE events, consisting of time-amplitude data, are treated as images by the model. Reasoning behind the CNN model and model efficacy will be discussed, along with [preliminary] results for EAS axis direction determination. This article will show that the accuracy of the model reaches 1 degrees-2 degrees for the zenith and azimuthal angles, however, the accuracy of the model does not reach the accuracy of conventional methods.
引用
收藏
页码：S32 / S36
页数：5
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