A deep learning code is developed to enhance HMI continuum intensity images and line-of-sight magnetic field for a better spatial resolution.
Employing an updated Babcock–Leighton dynamo model, this study finds that the model with scattered tilt angles, which are around the Joy’s Law but with a standard deviation of 15°, is able to reproduce the observed variations of solar cycles.
Long-term migration of the Sun’s open magnetic flux is studied, and its relation with the sunspot numbers is discussed.
Heat flux delivered by magnetic reconnection is calculated based on a model using magnetic field observations, and the calculation is then compared with AIA EUV observations.
Two flares occurred in a same active region above a same polarity inversion line, but one had a failed CME eruption but another one had a successful CME eruption. This study explored why that was the case.
Meridional flows during the solar minimum and maximum years are derived using 14 years of SOHO/MDI data. The flows changed significantly from the minimum to the maximum, and major changes were associated with the active latitudes.
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.