Segmentation and Tracking of Eruptive Solar Phenomena With Convolutional Neural Networks

Abstract Solar eruptive events are complex phenomena, which most often include coronal mass ejections (CME), CME‐driven compressive and shock waves, flares, and filament eruptions. CMEs are large eruptions of magnetized plasma from the Sun's outer atmosphere or corona, that propagate outward into the interplanetary space. Over the last several decades a large amount of remote solar eruption observational data has become available from ground‐based and space‐borne instruments. This has recently required the development of software approaches for automated characterization of eruptive features. Most solar feature detection and tracking algorithms currently in use have restricted applicability and complicated processing chains, while complexity in engineering machine learning (ML) training sets limit the use of data‐driven approaches for tracking or solar eruptive related phenomena. Recently, we introduced Wavetrack ‐ a general algorithmic method for smart characterization and tracking of solar eruptive features. The method, based on a‐trous wavelet decomposition, intensity rankings and a set of filtering techniques, allows to simplify and automate image processing a