Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network

TL;DR

This paper introduces a hybrid neural network model combining vision transformers and LSTMs to predict whether solar flares will be associated with CMEs, based on magnetogram data, achieving promising results.

Contribution

A novel deep learning approach that effectively predicts flare-CME associations using spatio-temporal patterns in solar magnetogram data.

Findings

HNN demonstrates good predictive performance.

Magnetic flux cancellation may trigger flare-associated CMEs.

Model captures key spatio-temporal patterns in magnetogram data.

Abstract

Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The association between flares and CMEs is not always obvious. In this study, we propose a new deep learning method, specifically a hybrid neural network (HNN) that combines a vision transformer with long short-term memory, to predict associations between flares and CMEs. HNN finds spatio-temporal patterns in the time series of line-of-sight magnetograms of solar active regions (ARs) collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory and uses the patterns to predict whether a flare projected to occur within the next 24 hours will be eruptive (i.e., CME-associated) or confined (i.e., not CME-associated). Our experimental results demonstrate the good performance of the HNN method. Furthermore, the results show that magnetic flux cancellation in polarity inversion line regions may well play a role in triggering flare-associated CMEs, a finding consistent with literature.