用机器学习分析荧光望远镜数据，识别宇宙射线轨迹并重建能量与方向
Analysis of Fluorescence Telescope Data Using Machine Learning Methods
- 用神经网络识别荧光望远镜中的广延大气簇射轨迹
- 实现对原初粒子能量和入射方向的准确重建
- 方法可推广至其他荧光望远镜，适合高能物理数据处理者
荧光望远镜是现代超高端宇宙射线实验中的关键仪器。本文以小型地面望远镜EUSO-TA的模拟数据为基础，尝试多种机器学习与神经网络方法，用于识别其数据中广延大气簇射的轨迹，并重建原初粒子的能量与到达方向。同时讨论该方法在其他荧光望远镜中的应用潜力，并提出改进路径，提升识别精度与计算效率。
原文摘要 · Abstract (English)
Fluorescence telescopes are among the key instruments used for studying ultra-high energy cosmic rays in all modern experiments. We use model data for a small ground-based telescope EUSO-TA to try some methods of machine learning and neural networks for recognizing tracks of extensive air showers in its data and for reconstruction of energy and arrival directions of primary particles. We also comment on the opportunities to use this approach for other fluorescence telescopes and outline possible ways of improving the performance of the suggested methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。