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.
AR12192, the largest active region in Solar Cycle 24, produced 6 X-class flares, but none of them were associated with a CME. However, a much weaker flare, of M4.0-class, was associated with a CME. Magnetic field and morphological changes are analyzed during these flares to understand why this is the case.
44 strong flares are analyzed, and a few factors are identified to determine whether a flare will be eruptive or confined.
Magnetic field changes associated with solar flares, observed by the SDO/HMI, are surveyed, and permanent changes of magnetic field are found in the majority of flare events. Properties of the magnetic field changes are further investigated.
Statistical studies find that white-light flares from the Sun and from solar-type stars have similar energy-duration relations, but the stellar flares have shorter duration. Cooling effect and stronger magnetic field in the stellar corona are proposed to explain this difference.
An X9.3 flare excited strong yet unusual sunquakes.
We attempt to forecast M- and X-class solar flares using a support vector machine algorithm and 4 years of HMI vector magnetic field data. With the true skill statistic as the preferred metric to estimate the algorithm performances, we obtain good predictive abilities. This may be partly due to fine-tuning the algorithm for this purpose, and to a choice of 13 SHARP parameters obtained from vector magnetograms.