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


AuthorStepanyuk, Oleg; Kozarev, Kamen; Nedal, Mohamed;
KeywordsParker Data Used; Coronal bright fronts; coronal mass ejections; image processing; eruptive filaments; CME; Astrophysics - Solar and Stellar Astrophysics; Astrophysics - Instrumentation and Methods for Astrophysics; Physics - Space Physics
AbstractCoronal 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 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 solar images of objects such as CME shock waves and filaments. 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 extracting 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. It holds the potential to prepare large training data sets for deep learning. We have implemented this method into a freely available Python library.
Year of Publication2022
JournalJournal of Space Weather and Space Climate
Volume12
Number of Pages20
Section
Date Publishedmay
ISBN
URL
DOI10.1051/swsc/2022020