Using 14 years of helioseismic and magnetic-field observations, this study shows that deeper subsurface outflows from active regions play an important role in transporting magnetic fluxes to the polar regions and regulating polar-field formation.
Spatiotemporal images of solar magnetic fluxes, made using the near-side magnetic-field observations and helioseismic far-side images, are analyzed separately using flux integration and power-spectrum analysis. Both results are consistent and imply that the surface active regions’ clustering patterns are likely imprints of magneto-Rossby waves in the tachocline.
Magnetofrictional simulations of Active Region 13500 reproduce its transition from stability to eruption and show that a massive sigmoid flux rope formed during the decay phase. The eruption began when the current-carrying helicity ratio reached about 0.3, indicating that helicity-based markers can help diagnose the eruptive potential of active regions.
HMI data from Solar Cycle 24 data are used to determine how often the Sun emerges sunspots in activity nests. It is found that the Sun shows moderate nesting behavior with 41% (48%) of AR magnetic flux found in northern (southern) hemisphere located in nests. The maximum number of nests are found with slightly prograde rotational velocities, and the nesting behavior is asymmetric in the hemispheres.
A robust, calibrated method for measuring sunspot areas from SOHO/MDI and SDO/HMI full-disk images enables a consistent, observer-independent, long-term catalogue of daily sunspot areas, revealing detailed patterns of sunspot group area evolution and solar cycle variability.
Through MHD simulations of flux eruptions, the study finds that the current helicity decreases prior to eruptions and then reverses to increase afterward. By examining multiple flare events, the authors identified observational evidence supporting these simulation results.
Multiple X-class flares and CMEs were produced by AR 13664/8 during the Mother’s Day week of 2024. This study suggests that predicting the locations of magnetically complex active regions, and studying and tracking their eruptive states using different proxy parameters can greatly improve the capability to forecast intense storms.
Each solar active region (AR) has its unique shape, size, and lifetime. In this work, the authors developed a method to ‘average’ bipolar ARs by normalizing their size, orientation, and timing, thereby revealing the typical properties of an AR and its evolutionary pattern.
It is widely known that magnetic fields suppress convection, but this effect has not been quantitatively assessed. Using vector magnetograms from HMI observations, the authors measured the advection speeds of magnetic flux as a function of field strength and found that the speeds steadily decrease with increasing field strength.
A deep convolutional neural network is trained using Hinode data that has been degraded to match the resolution and sensitivity of HMI observations. Once trained, the network can enhance HMI intensity and vector magnetic field data to the resolution and quality of Hinode.