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.
This study explores the magnetic triggers of recurrent active region jets. Both widely debated triggers, namely, flux cancellation and flux emergence, are associated alternatively to the apparently homologous jets.
Of three consecutive flares that occurred in a same active region within 4 hours, why were two non-eruptive and one eruptive? Non-linear force-free modeling suggest that breakout reconnection during the first two flares weakened the overlying field, allowing the flux rope to erupt in the third.
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.
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.
Both magnetic flux emergence and shearing flows occurred before the X9.3 flare on 2017 September 6. This analysis shows that shearing flows played a more significant role in leading to the helicity and electric currents buildup before the major eruption.
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.
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.
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.
The majority of flare forecasting methods rely on observations of magnetic field on the Sun’s surface, but which observable, Br or Blos, is a better predictor? Through comparing a few magnetic properties derived from both observables, this nugget gives some suggestion.