Automated Detection and Tracking of Solar Magnetic Bright Points

TL;DR

This paper introduces an automated algorithm for detecting and tracking Solar Magnetic Bright Points (MBPs) in high-resolution solar images, achieving about 90% detection accuracy in observational data.

Contribution

The paper presents a novel automated detection method for MBPs that combines intensity thresholding, gradient analysis, and object growing, improving detection efficiency.

Findings

Detects ~90% of MBPs in SST data

Effectively disentangles MBPs from bright granule pixels

Stabilizes images to account for variable seeing conditions

Abstract

Magnetic Bright Points (MBPs) in the internetwork are among the smallest objects in the solar photosphere and appear bright against the ambient environment. An algorithm is presented that can be used for the automated detection of the MBPs in the spatial and temporal domains. The algorithm works by mapping the lanes through intensity thresholding. A compass search, combined with a study of the intensity gradient across the detected objects, allows the disentanglement of MBPs from bright pixels within the granules. Object growing is implemented to account for any pixels that might have been removed when mapping the lanes. The images are stabilized by locating long-lived objects that may have been missed due to variable light levels and seeing quality. Tests of the algorithm employing data taken with the Swedish Solar Telescope (SST), reveal that ~90% of MBPs within a 75"x 75" field of view are detected.