SHINE: Multimodal Machine Learning Approaches for Solar Energetic Particles Events Predict — NSF Award to Utah State University (U
Solar Energetic Particle (SEP) events are bursts of high-energy particles from the Sun that can disrupt satellites, navigation systems, and human spaceflight. Predicting these events remains challenging because they are rare and driven by complex solar activity. This project will apply machine-learning methods to space
| Award title | SHINE: Multimodal Machine Learning Approaches for Solar Energetic Particles Events Predict |
|---|---|
| Award ID | 2501486 |
| Awardee | Utah State University |
| City | LOGAN |
| State | UT |
| Amount obligated | $580,185 |
| Principal investigator | Soukaina Filali Boubrahimi |
| Program | SOLAR-TERRESTRIAL |
| Start date | 03/15/2026 |
| Abstract | Solar Energetic Particle (SEP) events are bursts of high-energy particles from the Sun that can disrupt satellites, navigation systems, and human spaceflight. Predicting these events remains challenging because they are rare and driven by complex solar activity. This project will apply machine-learning methods to space-based observations spanning two solar cycles to improve identification of patterns that precede SEP events. By strengthening space weather forecasting, the research will enhance p |
| Source | NSF Awards |
$799/mo
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