Review paper details machine learning for solar energetic particle prediction

A new review paper published on arXiv details the application of machine learning models for predicting solar energetic particle (SEP) events. The manuscript, authored by Spiridon Kasapis, aims to consolidate current knowledge by identifying datasets, comparing model architectures, and outlining best practices for future research in this domain. The prediction of SEPs is crucial for safeguarding space technologies and human missions, as well as for advancing astrophysical understanding of particle acceleration and transport. AI

IMPACT Provides a consolidated overview of ML applications in space weather prediction, guiding future research in the field.

RANK_REASON The item is a review paper published on arXiv about machine learning models for a scientific prediction task. [lever_c_demoted from research: ic=1 ai=1.0]

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• Solar energetic particle (SEP)
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