HMI-observed vector magnetic-field maps were lowered to a resolution of lmax=5, so that a comparison between solar and stellar magnetic field is possible.
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.
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.
Where does a sunspot’s penumbra start to form, on the same side or the opposite side of its opposite-polarity sunspot? When does Evershed flow start to appear, before or after the penumbral formation? These questions are answered through analyzing selected samples observed by the HMI.