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.
<p align="justify">Solar eruptive events are complex phenomena, which most often include solar flares, filament eruptions, coronal mass ejections (CMEs), and CME-driven shock waves. CME-driven shocks in the corona and interplanetary space are considered to be the main producer of solar energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced. Current understanding points to CME-driven shocks and compressions in the solar corona.</p> <p align="justify">A CME kinematics shows three phases - an initial rising phase (weakly accelerated motion), an impulsive phase and a residual propagation phase with constant or decreasing speed.</p> <p align="justify">Despite significant amount of data available from ground-based (COSMO K-Cor, LOFAR) and remote instruments onboard of heliospheric space missions (SDO AIA, SOHO), processing of the data still requires noticeable effort. Most algorithms currently used in solar feature detection and tracking are known for their limited applicability and complexity of their processing chains, while usage of data-driven approaches for tracking of CME-related phenomena is currently limited due to insufficiency of training sets.</p> <p align="justify">Recently (Stepanyuk et.al, J. Space Weather Space Clim. Vol 12, 20(2022)), we have demonstrated the method and the software(https://gitlab.com/iahelio/mosaiics/wavetrack) for smart characterization and tracking of solar eruptive features based on the a-trous wavelet decomposition technique, intensity rankings and a set of filtering techniques. In this work we use Wavetrack to generate training sets for data-driven feature extraction and characterization. We utilize U-Net, a fully convolutional network which training strategy relies on the strong use of data augmentation to use the available annotated samples more efficiently. U-NET can be trained end-to-end from a very limited set of images, while feature engineering allows to improve this approach even further by expanding available training sets.</p> <p align="justify">Here we present pre-trained models and demonstrate data-driven characterization and tracking of solar eruptive features on a set of CME-events.</p>
<p>The shape and dynamics of coronal mass ejections (CMEs) varies significantly based on the instrument and wavelength used. This has led to significant debate about the proper definitions of CME/shock fronts, pile-up/compression regions, and cores observed in projection in optically thin vs. optically thin emission. Here we present an observational analysis of the evolving shape and kinematics of a large-scale CME that occurred on May 7, 2021 on the eastern limb of the Sun as seen from 1 au. The eruption was observed continuously, consecutively by the Atmospheric Imaging Assembly (AIA) telescope suite on the Solar Dynamics Observatory (SDO), the ground-based COronal Solar Magnetism Observatory (COSMO) K-coronagraph (K-Cor) on Mauna Loa, and the C2 and C3 telescopes of the Large Angle Solar Coronagraph (LASCO) on the Solar and Heliospheric Observatory (SoHO). We apply the recently developed Wavetrack Python suite for automated detection and tracking of coronal eruptive features to evaluate and compare the evolving shape of the CME front as it propagated from the solar surface out to 30 solar radii.</p>
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