Multi-scale image preprocessing and feature tracking for remote CME characterization

Abstract

Coronal Mass Ejections (CMEs) influence the interplanetary environment over vast distances in the solar system by injecting huge clouds of fast solar plasma and energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced, but current understanding points to CME-driven shocks and compressions in the solar corona. At the same time, unprecedented remote (AIA, LOFAR, MWA) and in situ (Parker Solar Probe, Solar Orbiter) solar observations are becoming available to constrain existing theories. Here we present a general method for recognition and tracking of objects on solar images – CME shock waves, filaments, active regions. The calculation scheme is based on a multi-scale data representation concept a-trous wavelet transform, and a set of image filtering techniques. We showcase its performance on a small set of CME-related phenomena observed with the SDO/AIA telescope. With the data represented hierarchically on different decomposition and intensity levels, our method allows to extract certain objects and their masks from the imaging observations, in order to track their evolution in time. The method presented here is general and applicable to detecting and tracking various solar and heliospheric phenomena in imaging observations. We implemented this method into a freely available Python library.

Multi-instrument observations and tracking of a coronal mass ejection front from low to middle corona

“…In this study, we applied it to the Wavetrack features obtained for each instrument's observations. More details and various aspects of using FLCT with Wavetrack are described in Stepanyuk et al (2022). We discuss below the individual results and how they compare among the instruments.…”

“…Recently, we proposed a method for smart characterization and tracking of solar eruptive features (Stepanyuk et al, 2022), and have shown its performance on a small set of CME-related phenomena observed with the AIA telescope. One of the purposes of that work was to create a tool that would make preparing CNN training sets a more easy task.…”

“…We applied an updated, multi-instrument version of the recently developed Wavetrack code for automated detection and tracking of coronal eruptive features (Stepanyuk et al, 2022) to evaluate and compare the evolving shape of the CME front as it propagated from the solar surface out to 20 solar radii. Initially, the framework performs wavelet decomposition of observational data and applies one or a few filtering techniques to each of the wavelet decomposition levels, with further recomposition and segmentation of the resulting image.…”

“…The framework follows a modular approach and is built as a set of classes where each class represents one or a few image processing techniques, so that these classes act as building blocks allowing various decomposition and processing configurations and setups, depending on the input image characteristics. The scheme in Figure 2 by Stepanyuk et al (2022) gives a general overview of the image processing stages with the Wavetrack software.…”

“…To each of the wavelet coefficients, a relative thresholding is applied once more depending on the statistical distribution of the pixel intensities for each of the decomposition levels. Finally, after segmentation of the object masks at each time step they are multiplied by the original data to reveal the intensity of different parts of the object (Stepanyuk et al, 2022) and provide data for the velocity field estimation. More detailed parameters of processing, decomposition, and filtering techniques are given in the examples section of the Wavetrack package 2 for the LASCO, K-Cor, and AIA instruments.…”