Automated Swirl Detection Algorithm (ASDA) and Its Application to Simulation and Observational Data

Abstract

Swirling motions in the solar atmosphere have been widely observed in recent years and suggested to play a key role in channeling energy from the photosphere into the corona. Here, we present a newly-developed Automated Swirl Detection Algorithm (ASDA) and discuss its applications. ASDA is found to be very proficient at detecting swirls in a variety of synthetic data with various levels of noise, implying our subsequent scientific results are astute. Applying ASDA to photospheric observations with a spatial resolution of 39.2 km sampled by the Solar Optical Telescope (SOT) on-board Hinode, suggests a total number of $1.62\times10^5$ swirls in the photosphere, with an average radius and rotating speed of $\sim290$ km and $< 1.0$ km s$^{-1}$, respectively. Comparisons between swirls detected in Bifrost numerical MHD simulations and both ground-based and space-borne observations, suggest that: 1) the spatial resolution of data plays a vital role in the total number and radii of swirls detected; and 2) noise introduced by seeing effects could decrease the detection rate of swirls, but has no significant influences in determining their inferred properties. All results have shown that there is no significant difference in the analysed properties between counter-clockwise or clockwise rotating swirls. About 70% of swirls are located in intergranular lanes. Most of the swirls have lifetimes less than twice of the cadences, meaning future research should aim to use data with much higher cadences than 6 s. In the conclusions, we propose some promising future research applications where ASDA may provide useful insights.