Authors: Gilchrst, S.A. (Planetary Science Institute), Odgren, M. (Planetary Science Institute, Evergreen High School), Welsch, B.T. (University of Wisconsin-Green Bay)

Sigmoids are S-shaped structures seen in X-ray and Extreme-Ultra Violet (EUV) images of the solar corona. They are precursors to eruptions and indicate the presence of highly non-potential structures in the corona, such as a flux rope. The presence of a sigmoid, therefore, should indicate the presence of significant electric currents in the corona.

The presence of strong coronal currents makes sigmoid regions a good candidate for the application of Gauss’s separation method (Schuck et al. 2022), which allows for the decomposition of a photospheric magnetogram into components due to currents above and below the photosphere. We refer to the component due to currents above the photosphere as the “photospheric imprint of coronal currents,” or just the imprint for short. For sigmoid regions, we might expect to see a significantly larger imprint than for non-sigmoid regions. Indeed, it is interesting to try and understand how this new measure of coronal non-potentiality (the imprint) compares to an old measure of coronal non-potentiality (the presence of a sigmoid).

We explore this relationship by performing a statistical comparison between the photospheric imprints for regions with and without a sigmoid present. We construct a sample of sigmoid regions from the catalog of Savcheva et al. (2014). In addition, we construct a “control” sample without sigmoids, using the same regions taken before the formation of the sigmoid.

For each region, we apply the Gauss separation method to a Helioseismic and Magnetic Imager (HMI) magnetogram and then characterize the distribution of the magnetic field using its high-order moments (Leka & Barnes 2003). We then perform a Bayesian statistical analysis of the moments for the two samples. Specifically, we compare a single population model to a two-population model.

This material is based upon work supported by the National Science Foundation under Grant No. 2302698. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.