用中子监测器产额函数和人工神经网络重建银河宇宙射线（GCR）谱：两种方法的比较
GCR Spectra Reconstructed with Neutron Monitor Yield Function and Artificial Neural Networks: Comparison of Two Methods
AI总结：
该研究利用校准产额函数加力场方案和人工神经网络两种方法，从全球中子监测网络重建GCR质子和氦能谱，再现太阳周期调制和短期扰动，人工神经网络性能出色，验证了中子监测器作为实时GCR光谱仪的有效性。
AI中文摘要：
我们提出了一个框架，可从全球中子监测网络重建时间分辨的银河宇宙射线（GCR）质子和氦能谱，在无直接卫星观测的情况下提供GCR通量数据。利用并比较了两种方法：校准产额函数加力场方案以及基于多站中子监测器计数率与日球物理指数训练的人工神经网络。重建的光谱时间序列再现了大规模太阳周期调制和短期扰动，并扩展到缺乏每日航天器数据的时期。人工神经网络在各能量下性能出色，平均绝对百分比误差显著更低，χ²/dof接近1。全面验证证实了其稳健性，并确立中子监测器为有效的实时GCR光谱仪，可用于多种目的。
英文摘要：
We present a framework that reconstructs time-resolved galactic cosmic-ray (GCR) proton and helium energy spectra from the global neutron monitor network, providing data about GCR flux without direct satellite observations. Two methods are utilized and compared: a calibrated yield function plus force-field scheme and artificial neural networks trained on multi-station neutron monitor count rates coupled with heliophysical indices. The reconstructed spectral time series reproduce both large-scale solar-cycle modulation and short-term disturbances and extend to periods lacking daily spacecraft data, including 2006-2011 (consistent with PAMELA) and 2019-2022 (consistent with AMS-02 Bartels rotation averages). Artificial neural networks deliver excellent performance across energies, with markedly lower mean absolute percentage error and $χ^2/\mathrm{dof}$ near unity. A thorough validation confirms robustness and establishes neutron monitors as an effective real-time GCR spectrometer that can be utilized for various purposes.
