Deep Learning the Forecast of Galactic Cosmic-Ray Spectra

TL;DR

This paper presents a deep learning LSTM-based model that predicts galactic cosmic-ray spectra using historical solar activity data, improving accuracy and providing a scalable alternative to traditional models for space weather forecasting.

Contribution

It introduces a novel LSTM framework that incorporates multiple solar parameters and historical cosmic-ray data for accurate short-term and long-term cosmic-ray flux predictions.

Findings

Achieves high accuracy in short-term and long-term cosmic-ray flux predictions.

Effectively incorporates multiple solar parameters and historical data.

Predicts full spectra for different particle species, aiding space weather forecasts.

Abstract

We introduce a novel deep learning framework based on Long Short-Term Memory (LSTM) networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in traditional transport models. By flexibly incorporating multiple solar parameters, such as the heliospheric magnetic field, solar wind speed, and sunspot numbers, our model achieves accurate short-term and long-term predictions of cosmic-ray flux. The addition of historical cosmic-ray flux data significantly enhances prediction accuracy, allowing the model to capture complex dependencies between past and future flux variations. Additionally, the model reliably predicts full cosmic-ray spectra for different particle species, enhancing its utility for comprehensive space weather forecasting. Our approach offers a scalable, data-driven alternative to traditional physics-based methods, ensuring robust daily and long-term forecasts. This work opens avenues for advanced models that can integrate broader observational data, with significant implications for space weather monitoring and mission planning.