Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images

TL;DR

Deep SWM leverages deep state space models and a novel pretraining strategy to improve long-range solar flare prediction from multi-wavelength solar images, outperforming existing methods and human experts.

Contribution

The paper introduces Deep SWM, a novel deep learning framework with a specialized pretraining approach for enhanced long-term solar flare prediction.

Findings

Deep SWM outperforms baseline models on standard metrics.

Deep SWM surpasses human expert performance.

The FlareBench dataset covers an 11-year solar cycle.

Abstract

Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often lack representation learning from solar images. On the other hand, end-to-end learning approaches struggle to model long-range temporal dependencies in solar images. In this study, we propose Deep Space Weather Model (Deep SWM), which is based on multiple deep state space models for handling both ten-channel solar images and long-range spatio-temporal dependencies. Deep SWM also features a sparse masked autoencoder, a novel pretraining strategy that employs a two-phase masking approach to preserve crucial regions such as sunspots while compressing spatial information. Furthermore, we built FlareBench, a new public benchmark for solar flare prediction covering a full 11-year solar activity cycle, to validate our method. Our method outperformed baseline methods and even human expert performance on standard metrics in terms of performance and reliability. The project page can be found at https://keio-smilab25.github.io/DeepSWM.