On the Prediction of >100 MeV Solar Energetic Particle Events Using GOES Satellite Data

TL;DR

This paper develops a decision tree-based method to predict >100 MeV solar energetic particle events using GOES satellite data, focusing on correlations across X-ray and proton channels to identify precursors for space weather disruptions.

Contribution

It introduces a novel multivariate time series approach that considers cross-channel correlations for SEP event prediction, which has not been explored before.

Findings

Proton channel correlations can serve as SEP precursors

Decision trees effectively predict >100 MeV SEP events

Cross-channel correlations improve prediction accuracy

Abstract

Solar energetic particles are a result of intense solar events such as solar flares and Coronal Mass Ejections (CMEs). These latter events all together can cause major disruptions to spacecraft that are in Earth's orbit and outside of the magnetosphere. In this work we are interested in establishing the necessary conditions for a major geo-effective solar particle storm immediately after a major flare, namely the existence of a direct magnetic connection. To our knowledge, this is the first work that explores not only the correlations of GOES X-ray and proton channels, but also the correlations that happen across all the proton channels. We found that proton channels auto-correlations and cross-correlations may also be precursors to the occurrence of an SEP event. In this paper, we tackle the problem of predicting >100 MeV SEP events from a multivariate time series perspective using easily interpretable decision tree models.