Neural networks trained on worldwide neutron-monitor counts plus solar indices reconstruct daily proton and helium cosmic-ray spectra for 2006–2022, matching PAMELA and AMS-02 data and beating a yield-function/force-field method by roughly 2–4× in error.
Solar-Terrestrial Physics , year = 2025, month = jun, volume =
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GCR Spectra Reconstructed with Neutron Monitor Yield Function and Artificial Neural Networks: Comparison of Two Methods
Neural networks trained on worldwide neutron-monitor counts plus solar indices reconstruct daily proton and helium cosmic-ray spectra for 2006–2022, matching PAMELA and AMS-02 data and beating a yield-function/force-field method by roughly 2–4× in error.