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Aims. We investigate how the splitting of the leading sunspot and associated flux emergence and cancellation in active region NOAA 11515 caused an eruptive M5.6 flare on 2012 July 2.
Methods. Continuum intensity, line-of-sight magnetogram, and dopplergram data of the Helioseismic and Magnetic Imager were employed to analyse the photospheric evolution. Filtergrams in H alpha and He I 10830 angstrom of the Chromospheric Telescope at the Observatorio del Teide, Tenerife, track the evolution of the flare. The corresponding coronal conditions were derived from 171 angstrom and 304 angstrom images of the Atmospheric Imaging Assembly. Local correlation tracking was utilized to determine shear flows.
Results. Emerging flux formed a neutral line ahead of the leading sunspot and new satellite spots. The sunspot splitting caused a long-lasting flow towards this neutral line, where a filament formed. Further flux emergence, partly of mixed polarity, as well as episodes of flux cancellation occurred repeatedly at the neutral line. Following a nearby C-class precursor flare with signs of interaction with the filament, the filament erupted nearly simultaneously with the onset of the M5.6 flare and evolved into a coronal mass ejection. The sunspot stretched without forming a light bridge, splitting unusually fast (within about a day, complete approximate to 6 h after the eruption) in two nearly equal parts. The front part separated strongly from the active region to approach the neighbouring active region where all its coronal magnetic connections were rooted. It also rotated rapidly (by 4.9 degrees h(-1)) and caused significant shear flows at its edge.
Conclusions. The eruption resulted from a complex sequence of processes in the (sub-)photosphere and corona. The persistent flows towards the neutral line likely caused the formation of a flux rope that held the filament. These flows, their associated flux cancellation, the emerging flux, and the precursor flare all contributed to the destabilization of the flux rope. We interpret the sunspot splitting as the separation of two flux bundles differently rooted in the convection zone and only temporarily joined in the spot. This explains the rotation as the continued rise of the separating flux, and it implies that at least this part of the sunspot was still connected to its roots deep in the convection zone.
In high-resolution solar physics, the volume and complexity of photometric, spectroscopic, and polarimetric ground-based data significantly increased in the last decade, reaching data acquisition rates of terabytes per hour. This is driven by the desire to capture fast processes on the Sun and the necessity for short exposure times "freezing" the atmospheric seeing, thus enabling ex post facto image restoration. Consequently, large-format and high-cadence detectors are nowadays used in solar observations to facilitate image restoration. Based on our experience during the "early science" phase with the 1.5 m GREGOR solar telescope (2014–2015) and the subsequent transition to routine observations in 2016, we describe data collection and data management tailored toward image restoration and imaging spectroscopy. We outline our approaches regarding data processing, analysis, and archiving for two of GREGOR's post-focus instruments (see http://gregor.aip.de), i.e., the GREGOR Fabry–Pérot Interferometer (GFPI) and the newly installed High-Resolution Fast Imager (HiFI). The heterogeneous and complex nature of multidimensional data arising from high-resolution solar observations provides an intriguing but also a challenging example for "big data" in astronomy. The big data challenge has two aspects: (1) establishing a workflow for publishing the data for the whole community and beyond and (2) creating a collaborative research environment (CRE), where computationally intense data and postprocessing tools are colocated and collaborative work is enabled for scientists of multiple institutes. This requires either collaboration with a data center or frameworks and databases capable of dealing with huge data sets based on virtual observatory (VO) and other community standards and procedures.