Constraining the global heliospheric transport of galactic cosmic rays in solar cycles 23 and 24

TL;DR

This paper uses advanced modeling and machine learning to analyze how galactic cosmic rays are affected by solar activity during solar cycles 23 and 24, providing insights into their transport mechanisms.

Contribution

It introduces a neural-network-accelerated Markov chain Monte Carlo method to constrain GCR transport coefficients over time using observational data.

Findings

Constrained the rigidity dependence of GCR diffusion coefficients.

Revealed variations in transport parameters across solar cycles 23 and 24.

Improved understanding of solar modulation effects on cosmic rays.

Abstract

Galactic cosmic rays (GCRs) are affected by solar modulation while they propagate through the heliosphere. The study of the time variation of GCR spectra observed at Earth can shed light on the underlying physical processes, specifically diffusion and particle drifts. We combine a state-of-the art 3D numerical model of GCR transport in the heliosphere with a neural-network-accelerated Markov chain Monte Carlo to constrain the rigidity and time dependence of the global transport coefficients, using precise GCR data from the PAMELA and AMS-02 experiments between 2006 and 2019.