Automated Detection of Coronal Loops using a Wavelet Transform Modulus Maxima Method

TL;DR

This paper introduces a wavelet transform modulus maxima method for automated detection of coronal loops in solar images, aiming to improve accuracy and efficiency over previous algorithms.

Contribution

It presents a novel wavelet-based approach for detecting coronal loops, optimizing parameters to enhance detection completeness and reduce noise interference.

Findings

Effective detection of coronal loops in EUV images

Comparison shows improved performance over previous methods

Optimized parameters increase detection accuracy

Abstract

We propose and test a wavelet transform modulus maxima method for the au- tomated detection and extraction of coronal loops in extreme ultraviolet images of the solar corona. This method decomposes an image into a number of size scales and tracks enhanced power along each ridge corresponding to a coronal loop at each scale. We compare the results across scales and suggest the optimum set of parameters to maximise completeness while minimising detection of noise. For a test coronal image, we compare the global statistics (e.g., number of loops at each length) to previous automated coronal-loop detection algorithms.