Work

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

Year: 2026

Type: article

Abstract: 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 / d o f 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.

Source: The Open Journal of Astrophysics

DOI: 10.33232/001c.165496

Authors: Stepan A. Siruk, Vladislav V. Alekseev, Victor A. Kuzminov, Rustam F. Yulbarisov, Andrey G. Mayorov

Institutions: National Research Nuclear University MEPhI, Yaroslavl State University, State Scientific Research Institute of Aviation Systems

Language: English

Topic: Solar and Space Plasma Dynamics

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funder: Russian Science Foundation

OpenAlex ID: W7170588964

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