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Interpretable Machine Learning to Forecast SEP Events for Solar Cycle 23
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  • Spiridon Kasapis,
  • Lulu Zhao,
  • Yang Chen,
  • Xiantong Wang,
  • Monica Bobra,
  • Tamas I. I. Gombosi
Spiridon Kasapis
University of Michigan

Corresponding Author:[email protected]

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Lulu Zhao
University of Alabama in Huntsville
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Yang Chen
University of Michigan
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Xiantong Wang
University of Michigan
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Monica Bobra
Stanford University
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Tamas I. I. Gombosi
University of Michigan-Ann Arbor
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Abstract

We use machine learning methods to predict whether an active region (AR) which produces flares will lead to a solar energetic particle (SEP) event using Space-Weather Michelson Doppler Imager (MDI) Active Region Patches (SMARPs). This new data product is derived from maps of the solar surface magnetic field taken by the Michelson Doppler Imager (MDI) aboard the Solar and Heliospheric Observatory (SOHO). We survey the SMARP active regions associated with flares that appear on the solar disk between June 5, 1996 and August 14, 2010, label those that produced SEPs as positive and the rest as negative. The AR SMARP features that correspond to each flare are used to train two different types of machine learning methods, the support vector machines (SVMs) and the regression models. The results show that the SMARP data can predict whether a flare will lead to an SEP with accuracy (ACC) {less than or equal to}0.72{plus minus}0.12 while allowing for a competitive leading time of 55.3{plus minus}28.6 minutes for forecasting the SEP events.
Feb 2022Published in Space Weather volume 20 issue 2. 10.1029/2021SW002842