Using Machine Learning Methods to Forecast If Solar Flares Will Be Associated with CMEs and SEPs

Abstract

Among the eruptive activity phenomena observed on the Sun, the most technology threatening ones are flares with associated coronal mass ejections (CMEs) and solar energetic particles (SEPs). Flares with associated CMEs and SEPs are produced by magnetohydrodynamical processes in magnetically active regions (ARs) on the Sun. However, these ARs do not only produce flares with associated CMEs and SEPs, they also lead to flares and CMEs, which are not associated with any other event. In an attempt to distinguish flares with associated CMEs and SEPs from flares and CMEs, which are unassociated with any other event, we investigate the performances of two machine learning algorithms. To achieve this objective, we employ support vector machines (SVMs) and multilayer perceptrons (MLPs) using data from the Space Weather Database of Notification, Knowledge, Information (DONKI) of NASA Space Weather Center, {\it the Geostationary Operational Environmental Satellite} ({\it GOES}), and the Space-Weather Heliospheric and Magnetic Imager Active Region Patches (SHARPs). We show that True Skill Statistics (TSS) and Heidke Skill Scores (HSS) calculated for SVMs are slightly better than those from the MLPs. We also show that the forecasting time frame of 96 hours provides the best results in predicting if a flare will be associated with CMEs and SEPs (TSS=0.92$\pm$0.09 and HSS=0.92$\pm$0.08). Additionally, we obtain the maximum TSS and HSS values of 0.91$\pm$0.06 for predicting that a flare will not be associated with CMEs and SEPs for the 36 hour forecast window, while the 108 hour forecast window give the maximum TSS and HSS values for predicting CMEs will not be accompanying any events (TSS=HSS=0.98$\pm$0.02).

References in corpus (10)

• Scikit-learn: Machine Learning in Python
• The Solar Cycle
• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• The Two Sources of Solar Energetic Particles
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Predicting Coronal Mass Ejections Using Machine Learning Methods
• SunPy - Python for Solar Physics
• Reconciliation of Waiting Time Statistics of Solar Flares Observed in Hard X-Rays
• Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression

Cited by in corpus (9)

• The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• The data-driven future of high energy density physics
• Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
• Identification of BASS DR3 Sources as Stars, Galaxies and Quasars by XGBoost
• Distinguishing between Flaring and Non-Flaring Active Regions: A Machine Learning Perspective
• Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
• Solar flare forecasting using morphological properties of sunspot groups
• Predicting Solar Flares with Remote Sensing and Machine Learning