Complex Network View of the Sun's Magnetic Patches: I. Identification

TL;DR

This paper introduces a novel complex network-based method to identify solar magnetic patches from magnetogram data, effectively recognizing features of various sizes with low error rates, enhancing solar magnetic feature detection.

Contribution

The study develops a new complex network approach for solar magnetic patch identification, enabling size-independent detection and comparison with existing methods.

Findings

Successfully identified magnetic patches of various sizes.

Achieved approximately 8% false-positive and 1% false-negative rates for small features.

Demonstrated the method's effectiveness through feature tracking and comparison.

Abstract

Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star's atmospheric conditions. Hence, detecting and identifying these features (also known as magnetic elements) are among the essential topics in the community. Here, we apply the complex network approach to recognize the solar magnetic patches. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network approach also provides the ability to rank the patches based on their connectivity (i.e., degree of nodes) and importance (i.e., PageRank). The use of the developed algorithm in the identification of magnetic patches is examined by tracking the features in consecutive frames, as well as making a comparison with the other approaches to identification. We find that this method could conveniently identify features regardless of their sizes. For small-scale (one or two pixels) features, we estimate the average of 8% false-positive and 1% false-negative errors.