用神经网络重建荧光望远镜记录uhecr的能量和到达方向

IF 0.4 4区 物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY
Mikhail Zotov, for the JEM-EUSO Collaboration
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引用次数: 0

摘要

荧光望远镜是现代实验中广泛使用的重要仪器,用于记录由超高能量宇宙射线产生的大量空气簇射(EASs)的紫外线辐射。我们提出了概念验证卷积神经网络,旨在利用国际JEM-EUSO合作开发的两台望远镜的模型数据重建初级粒子的能量和到达方向。我们还演示了如何使用简单的卷积编码器-解码器进行EAS跟踪识别。该方法具有通用性,可用于其他荧光望远镜。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reconstruction of Energy and Arrival Directions of UHECRs Registered by Fluorescence Telescopes with Neural Networks

Reconstruction of Energy and Arrival Directions of UHECRs Registered by Fluorescence Telescopes with Neural Networks

Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultrahigh energies. We present proof-of-concept convolutional neural networks aimed at reconstruction of energy and arrival directions of primary particles using model data for two telescopes developed by the international JEM-EUSO collaboration. We also demonstrate how a simple convolutional encoder-decoder can be used for EAS track recognition. The approach is generic and can be adopted for other fluorescence telescopes.

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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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