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.
High-latitude m=1 inertial modes were analyzed and characterized using the time-distance helioseismic subsurface flows. It was found that the mode’s power exhibits an anti-correlation with solar activity. Magnetic flux transported from low to high latitudes influences both the mode’s power and lifetime, enhancing its power and shortening its lifetime upon arrival.
Strong acoustic scatterers are found beneath sunspot umbrae and penumbrae using the phase-correlation holography method. These scatterers indicate the fragmentation of magnetic flux, as suggested by Eugene Parker in the 1970s.
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.
Eruptive and non-eruptive solar flares were investigated based on an analysis of electric current neutralization and torus instability. Combined analysis of these factors offers a more reliable prediction of eruptive events than relying on either one alone.
The analysis of magnetic fields and electrical currents indicates that a specific configuration – currents in opposite magnetic regions flowing in the same direction and peaking concurrently – might create favorable conditions for the magnetic reconnection process that powers solar flares.