most citedReview of Machine Learning Models for Solar Energetic Particle Prediction

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

astro-ph.SR2026

SEP-PRISM Data: A multi-source dataset for solar energetic particle forecasting

Yian Yu, Yang Chen, Lulu Zhao +3

Solar energetic particle (SEP) event forecasting often involves integrating heterogeneous observations that differ in cadence, temporal coverage, format, and historical availabilit…

astro-ph.SR2026

Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning

Yian Yu, Yang Chen, Lulu Zhao +3

Solar energetic particle (SEP) events, defined by proton flux exceeding 10 pfu in the > 10 MeV channel, pose major risks to spacecraft operations, astronaut safety, and high-latitu…

astro-ph.SR20261 cited

Review of Machine Learning Models for Solar Energetic Particle Prediction

Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman +73

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond…

astro-ph.SR2025

Operational and Exploration Requirements and Research Capabilities for SEP Environment Monitoring and Forecasting

Viacheslav Sadykov, Petrus Martens, Dustin Kempton +15

Mitigating risks posed by solar energetic particles (SEPs) to operations and exploration in space and Earth's atmosphere motivates the development of advanced, synergistic approach…

astro-ph.SR2025

Physics-Based Simulation of the 2013 April 11 Solar Energetic Particle Event

Weihao Liu, Igor V. Sokolov, Lulu Zhao +11

Solar energetic particles (SEPs) can pose hazardous radiation risks to both humans and spacecraft electronics in space. Numerical modeling based on first principles offers valuable…