arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2408.02440astro-ph.IM
收藏

利用神经网络重建荧光望远镜记录的UHECR的能量和到达方向

Reconstruction of energy and arrival directions of UHECRs registered by fluorescence telescopes with a neural network

Mikhail Zotov

AI总结:

本研究提出一种概念验证卷积神经网络,基于JEM-EUSO合作组的两台望远镜模型数据重建超高能宇宙射线的能量与到达方向,还验证了卷积编码器-解码器的EAS径迹识别能力,方法可推广至其他荧光望远镜。

摘要:

荧光望远镜是现代实验中广泛使用的重要仪器,用于记录由超高能宇宙射线产生的广延空气簇射(EAS)的紫外辐射。我们提出了一种概念验证型卷积神经网络,旨在利用国际JEM-EUSO合作组研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。我们还展示了如何将简单的卷积编码器-解码器用于EAS径迹识别。该方法具有通用性,可应用于其他荧光望远镜。

英文摘要:

Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultra-high energies. We present a proof-of-concept convolutional neural network 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑