A forecasting framework for galactic cosmic ray flux in space weather applications

TL;DR

This paper presents a new forecasting framework for galactic cosmic ray flux that leverages solar activity proxies and advanced time-series analysis to improve space weather predictions and radiation risk assessments.

Contribution

It introduces a generalizable long-term cosmic ray flux forecasting strategy based on delayed cross-correlation and signal processing techniques, building on previous models.

Findings

The framework accurately reproduces observed cosmic ray flux data.

It demonstrates strong potential for space radiation monitoring and forecasting.

The approach captures long-term variability of galactic cosmic rays.

Abstract

The intensity and energy spectrum of galactic cosmic rays in the heliosphere are significantly influenced by the 11-year solar cycle, a phenomenon known as solar modulation. Understanding this effect and its underlying physical mechanisms is essential for assessing radiation exposure and associated risks during space missions. Starting from a previously developed effective predictive model of solar modulation, validated using cosmic ray flux measurements from space-based detectors such as PAMELA and AMS-02, we build a generalizable forecasting strategy for the long-term evolution of cosmic ray fluxes. This strategy is based on identifying delayed cross-correlation relationships between solar proxies and the model's parameters. It integrates recent findings on time lags between cosmic ray fluxes and solar activity, and incorporates advanced time-series signal processing techniques. The framework not only performs well in reproducing observed data, but also shows strong potential for applications in space radiation monitoring and forecasting. By efficiently capturing the long-term variability of galactic cosmic rays, our approach contributes valuable insights for evaluating radiation risks, ultimately supporting safer and more effective space exploration.