2 articles tagged with #space-weather. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
This arXiv paper reviews machine learning models designed to predict solar energetic particle (SEP) events, which pose radiation risks to aviation, spacecraft, and human space exploration. The study compares ML architectures, training datasets, and methodologies against traditional physics-based approaches, providing recommendations for future research in SEP forecasting.
Researchers developed a diffusion model-based framework called CH-aware DMT that reconstructs synthetic SDO/AIA 193 Å EUV solar images from historical He I 10830 Å observations, enabling coronal analysis extending back decades before modern EUV imaging became available. The model achieves high fidelity on test data (CC=0.92 for full-disk morphology) and demonstrates physical plausibility when validated against SOHO, Yohkoh, and long-term solar activity proxies spanning 1974-2015.
