This study explores the magnetic triggers of recurrent active region jets. Both widely debated triggers, namely, flux cancellation and flux emergence, are associated alternatively to the apparently homologous jets.
Of three consecutive flares that occurred in a same active region within 4 hours, why were two non-eruptive and one eruptive? Non-linear force-free modeling suggest that breakout reconnection during the first two flares weakened the overlying field, allowing the flux rope to erupt in the third.
Helicity injection by the continued shear and converging flows contributes to a sigmoid’s sustenance, its core field twist, ans its eventual eruption.
Magnetic flux of opposite polarities belonging to two different emerging/emerged bipoles inside multipolar magnetic regions, can experience “collisional shearing”, a process resulting in strong shearing and fast cancellation of magnetic flux near the polarity inversion line. This type of flux cancellation is found to be the cause of a succession of major flares and CMEs in complex active regions.
Both magnetic flux emergence and shearing flows occurred before the X9.3 flare on 2017 September 6. This analysis shows that shearing flows played a more significant role in leading to the helicity and electric currents buildup before the major eruption.
A total of 90 circular-ribbon flares are identified in 8 years of SDO observations, and 33 of them are found associated with white-light enhancements, a rate higher than non-circular-ribbon flares. It is thus suggested that the fan-spine magnetic field topology and the total amount of energy release plays roles in causing white-light flares.
Analysis of HMI and KONUS/WIND data shows that photospheric and helioseismic flare impacts started to develop in compact regions in close vicinity of the magnetic polarity inversion line in the pre-impulsive phase before detection of hard X-ray emission.
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.