103. Enhancing SDO/HMI Images Using Deep Learning
A deep learning code is developed to enhance HMI continuum intensity images and line-of-sight magnetic field for a better spatial resolution.
102. Solar magnetic cycle and its variability in a 3D Babcock-Leighton dynamo model
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.
101. Solar Open Flux Migration from Pole to Pole: Magnetic Field Reversal
Long-term migration of the Sun’s open magnetic flux is studied, and its relation with the sunspot numbers is discussed.
100. Observationally Quantified Reconnection Providing a Viable Mechanism for Active Region Coronal Heating
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.
99. A Comparative Study between A Failed and A Successful Eruption Initiated from the Same Polarity Inversion Line in AR 11387
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.
98. Solar-Cycle Variations of Meridional Flows in the Solar Convection Zone with Helioseismic Methods
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.
97. Successive X-class Flares and Coronal Mass Ejections Driven by Shearing Motion and Sunspot Rotation in Active Region NOAA 12673
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.
96. Evaluation of Applicability of a Flare Trigger Model Based on a Comparison of Geometric Structures
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.
95. Forecasting Solar Flares Using Magnetogram-Based Predictors and Machine Learning
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.
94. Deep Learning Based Solar Flare Forecasting Model: Results for Line-of-Sight Magnetograms
A deep-learning method, Convolutional Neural Network, is developed to use the HMI’s line-of-sight magnetic field to forecast solar flares.