Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning
Researchers have developed a new deep learning framework called SEPNET-PRISM to improve the 24-hour forecasting of solar energetic particle events. By integrating multi-source solar data, the model provides more accurate predictions of proton and X-ray fluxes to help protect spacecraft and aviation.
Why it matters
Enhanced space weather forecasting is critical for safeguarding global satellite infrastructure and astronaut safety from solar radiation risks.
Scientific Reports ( 2026 ) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
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