Shearing motions and sunspot rotations found in NOAA AR 12673 are believed to lead the free energy buildup and flux rope formation, which are responsible for the two successive X-class flares.
A sample of 32 flare events are analyzed to evaluate how these events agree with a flare-triggering model, which examines shear angles of large-scale magnetic field and small-scale dipole field during the flares’ precursor brightening.
A set of parameters that characterize the complexity and energy potential of solar active-regions is fed through several Machine Learning and conventional statistics algorithms to forecast solar flares.
A deep-learning method, Convolutional Neural Network, is developed to use the HMI’s line-of-sight magnetic field to forecast solar flares.