Magnetic-field dependence of active regions’ tilt angles are analyzed using the MDI and HMI observations for two solar cycles. The variation of the tilt angles with the maximum magnetic-field strength of the ARs indicates a nonlinear tilt quenching in the Babcock–Leighton process.
Similar to sunspots, the stable regions of pores on the Sun are also found to be defined by a critical value of the vertical component of the magnetic field. The critical value is comparable to that found in stable sunspots.
To search for signatures of Alfvénic waves in the solar photosphere, the authors analyze the oscillation amplitudes, phases and time-distance behavior between different observables in a sunspot umbra, its polarity inversion line, and surrounding area.
Through studying three homologous eruptive events in an active region, the authors conclude that shearing motions and magnetic flux cancellation play a dominant role leading to the recurrent eruptions, and are key processes forming the eruptive structures.
Using the solar axial magnetic dipole moment obtained prior to the solar minimum, the author predicts that the maximum sunspot number of Solar Cycle 25 is about 128.
Analysis of magnetic helicity of eruptive and confined flaring events indicates that non-potential magnetic helicity is indicative to eruptive potentials of active regions.
In an MHD simulation of flux emergence, a δ-sunspot is formed spontaneously by a collision of areas with opposite polarities. Driven by convective flows and counter-streaming flows, sheared polarity inversion lines form and flux ropes are created above.
What excites the sunspot umbral oscillations? Through analyzing two sunspots observed by FeI line, the authors found that the 3-minute umbral oscillations are likely excited by internal small-scale magnetoconvection associated with umbral dots.
A number of sunquake events were detected in the photosphere after the X9.3 flare of 6 September 2017. This analysis reported the first detection of the chromspheric response to the sunquake events using CaII and Hα observations made by the Swedish 1-meter Solar Telescope.
A deep learning code is trained using the Sun’s front-side observations, HMI’s magnetograms and AIA’s 304Å EUV images, to establish a relation between magnetic field and EUV flux. Then the code is applied on the STEREO/EUVI 304Å data to obtain the Sun’s far-side magnetic field.