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Validation of the neutron monitor yield function using data from AMS-02 experiment, 2011--2017

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arxiv 1904.01929 v1 pith:LHOCKXUE submitted 2019-04-03 physics.space-ph astro-ph.EP

classification physics.space-phastro-ph.EP
keywords yielddatafunctionspectraams-02measuredmi13analysis
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The newly published spectra of protons and helium over time directly measured in space by the AMS-02 experiment for the period 2011--2017 provide a unique opportunity to calibrate ground-based neutron monitors (NMs). Here, calibration of several stable sealevel NMs (Inuvik, Apatity, Oulu, Newark, Moscow, Hermanus, Athens) was performed using these spectra. Four modern NM yield functions were verified: Mi13 (Mishev et al., 2013), Ma16 (Mangeard et al., 2016), CM12 (Caballero-Lopez & Moraal, 2012) and CD00 (Clem & Dorman, 2000), on the basis of the cosmic-ray spectra measured by AMS-02. The Mi13 yield function was found to realistically represent the NM response to galactic cosmic rays. CM12 yield function leads to a small skew in the solar cycle dependence of the scaling factor. In contrast, Ma16 and CD00 yield functions tend to overestimate the NM sensitivity to low-rigidity (<10 GV) cosmic rays. This effect may be important for an analysis of ground level enhancements, leading to a potential underestimate of fluxes of solar energetic particles as based on NM data. The Mi13 yield function is recommended for quantitative analyses of NM data, especially for ground-level enhancements. The validity the force-field approximation was studied, and it was found that it fits well the directly measured proton spectra, within a few % for periods of low to moderate activity and up to ~10% for active periods. The results of this work strengthen and validate the method of the cosmic-ray variability analysis based on the NM data and yield-function formalism, and improves its accuracy.

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  1. GCR Spectra Reconstructed with Neutron Monitor Yield Function and Artificial Neural Networks: Comparison of Two Methods

    astro-ph.IM 2026-07 conditional novelty 5.0 of 10

    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-f...

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