A Code for Automated Tracing of Coronal Loops Approaching Visual Perception

Abstract

We develop a new numerical code with automated feature extraction, customized for tracing of coronal loops, a method we call Oriented Coronal CUrved Loop Tracing (OCCULT), which for the first time breaks even with the results of visual tracing. The method used is based on oriented-directivity tracing of curvi-linear features, but in contrast to other general feature-extraction algorithms, it is customized for solar EUV and SXR images by taking advantage of the specific property that coronal loops have large curvature radii compared with their widths. We evaluate the performance of this new code by comparing the cumulative distribution of loop lengths, the median and maximum loop lengths, the completeness of detection, and the congruency of the detected features with other numerical codes and visual tracings. We find that the new code closely approaches the results of visual perception and outperforms the other existing numerical codes. This algorithm is useful for the 3D reconstruction of the geometry, motion, and oscillations of coronal loops, with single or stereoscopic spacecraft, as well as for modeling of the loop hydrodynamics and the coronal magnetic field.