Abstract

Motions of sunspots cause the coronal magnetic field to become deformed with the effect that energy can be stored in the coronal magnetic field. This energy can be released through events such as solar flares. Sunspots are known to rotate about their umbral centres, and this rotation contributes to the build-up of energy within an active region that can be released during an eruptive solar event. To understand the relationship between rotational forms of sunspot dynamics and solar activity, a large, unbiased statistical survey of sunspots is desirable.

To generate such a statistical sample, a fully automatic sunspot identification and tracking method, SSTrack, is developed. This method is tested on a previously analysed four-month sample of active regions generated using a semi-automatic sunspot rotation tool. The new method is designed to be applied to long periods of observations so that large samples can be efficiently obtained, while working at a high-cadence to effectively capture fine-scale-behaviour, such as sunspot splitting and mergers. SSTrack is able to identify fifty-four of the fifty-six sunspots in the four-month sample as well as an additional forty-three sunspots not found by the semi-automatic method. It is able to identify many of the fifty-four commonly found sunspots earlier and track them for longer than the semi-automatic method. The rotation about umbral centres is calculated for each sunspot using the tracking data from both methods, and when considering only overlapping observations per sunspot the methods show good agreement. The semi-automatic method is found to detect greater rotation when a sunspot undergoes fragmentation due to the changing structure overly influencing the determination of the centre of the sunspot.