AI 中文总结
本研究以FAST望远镜单构型模拟数据为基础，运用机器学习技术重建广延空气簇射参数，实现了亚百分比的能量分辨率，为FAST观测站的研发提供了关键验证与经验。
AI 中文摘要
我们展示了单望远镜构型下的单像素望远镜荧光探测器阵列（FAST）观测站的性能，这是迈向未来用于探测超高能宇宙射线的大视场观测站的重要一步。在低强度瞬态信号这一具有挑战性的领域中，我们利用机器学习技术对无噪声模拟事件开展了主要簇射物理参数的重建研究。我们发现，即便仅使用单台FAST望远镜的四个光电倍增管的信息，通过各类人工深度神经网络和卷积神经网络架构，簇射的真实能量与重建能量仍存在极佳的相关性；而对于簇射发展最大值Xmax，其性能有所下降，我们还将结果与基准梯度提升回归模型进行了对比。能量的分辨率达到亚百分比水平，而Xmax的分辨率约为5%。能量的预测值与真实值的相对差异低于1%，而Xmax的相对差异范围为-8%至+17%，这可归因于单台FAST望远镜构型的信息有限。这些结果构成了重要的性能验证，并为已建成的FAST原型以及正在建造的FAST观测站的更复杂构型提供了可借鉴的经验。
英文摘要
We present the capabilities of the Fluorescence detector Array of Single-pixel Telescopes (FAST) observatory in the single telescope configuration as an important step towards the possible future large-field observatory for detecting ultra-high-energy cosmic rays. Reconstruction of main shower physics parameters are explored on noise-free simulated events using machine learning techniques in the challenging domain of a low-intensity transient signal. We find a very good correlation between the true and reconstructed energy of the shower even with the information from just the four photomultipliers of the single FAST telescope, and a reduced performance for the maximum of the shower development Xmax, using various architectures of artificial deep and convolutional neural networks, with a comparison to a benchmark gradient boost regression model. The resolution in the energy is found at the sub-percent level, while in Xmax it is~$5\%$. The relative difference between predicted and true values is under one percent for energy, while for Xmax it ranges from $-8\%$ to $+17\%$, which can be attributed to the limited information from the single FAST telescope configuration. The results constitute an important capabilities verification and a lesson learned with implications for established FAST prototypes as well as for more complex configurations of the FAST observatory under construction.
