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

页码：S32 / S36

页数：5

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