Prediction of solar energetic particle radiation timing and dosage using physics-guided machine learning algorithms with remote observations of the solar photosphere, corona and interplanetary medium

Solar energetic particles are high-energy radiation emitted from the sun during solar flares or coronal mass ejections. Once they are released from the sun, they travel through the magnetic fields of the solar corona and interplanetary medium. The radiation level at Earth can rise as soon as within eight minutes after events on the sun, but it may take hours to days to reach peak intensity and eventually subdue. The radiations are extremely harmful to astronauts working in space and electronic components on spacecraft, and airplane passengers on polar routes may also experience the radiation. To develop the software tools for forecasting solar energetic particle radiation, we apply machine learning to a large amount of observational and theoretical model data accumulated over the past several decades. The expected software will allow us to use real-time observations of solar corona mass ejections, solar flare x-ray, radio emissions, relativistic electrons, magnetic field, plasma, and other relevant measurements to forecast possible occurrence, timing and radiation level on the continuous-time bases.

This proposed model will forecast the occurrence and flux of >10 MeV particles (S1-S5 event on the NOAA space weather prediction center solar radiation scale). A successful forecast will help protect the health of astronauts during a lunar mission and valuable instruments on spacecraft. The generated/engineered new and unanticipated patterns/features that are extracted from the machine learning procedure will further help understand the properties of SEPs and their underlying physical processes.

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