This analysis shows that a new bipolar emergence, whose positive polarity collided with the pre-existing negative polarity, in AR11283 led to energy and helicity buildup in the form of magnetic flux ropes. Recurrent energy releases caused a few homologous CMEs from this region.
Sunquakes are helioseismic waves excited by solar flares, usually observed in the photosphere. However, some of these events are found to have their counterparts in the chromosphere, as observed in the SDO/AIA UV channels.
An unprecedented observation of a limb flare in HMI’s white-light continuum shows that the white-light intensity at the post-flare loop-top continues to grow for 16 more minutes while UV/EUV intensities decay. Both the WL/UV intensity and the EUV intensities show quasi-periodic pulsations with a period close to 8.0 and 6.8 minutes, respectively.
A statistical study of hundreds of solar flares, with or without CMEs associated with them, indicates the larger the total magnetic flux of the flare-host active region, the less likely the flare is associated with a CME.
Why do some flares cause sunquakes and others do not? A survey of 60 strong flares in Solar Cycle 24 supports a hypothesis that the coupling of downward photospheric oscillations and the impacts from flares may play a role in causing sunquakes.
A sunquake event was excited by an M9.3 flare; however, the source of the sunquake waves was wave-mechanically extrapolated to about 1 megameter beneath the photosphere.
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.
Analysis of magnetic helicity of eruptive and confined flaring events indicates that non-potential magnetic helicity is indicative to eruptive potentials of active regions.
Critical decay index is a measure of the rate at which background field intensity decreases with height over the flux ropes or erupting structures. The indices for 10 eruptive prominences are calculated, and their relations to the eruptions are discussed.
An unsupervised machine-learning algorithm is used on selected features derived from the polarity inversion lines (PIL) mask and difference PIL mask. It is found these features are effective in predicting flaring occurrences.