Structure and dynamics of erupting solar prominences using the Rolling Hough Transform: Toward a feature-oriented classification

Abstract

The classification of solar prominences has proven to be challenging due to their diverse morphologies and dynamical behaviour. Complexity is heightened when considering eruptive prominences, where the dynamics demand methods capable of capturing detailed structural information. While there exists a range of line-of-sight (LOS) and plane-of-sky (POS) techniques which have advanced our understanding of prominence motions, they are subject to limitations, emphasising the need for effective methods of extracting structural information from prominence dynamics. We present a proof-ofconcept for the spatial Rolling Hough Transform (RHT) algorithm, which identifies finescale structural orientation in the POS, applied to prominence structure and dynamics. We demonstrate the RHT approach using two contrasting prominence dynamics events using SDO/AIA 304 Å observations: (1) a quiet-Sun eruption, (2) activation (swirl) of a polar-crown prominence. By analysing the light curves and movies from each event, we divide the events into distinct dynamical phases: from slow rise to drainage. The spatial RHT method enables us to extract structural information and localised dynamics for both events and the different evolution phases. We develop a classification to label the prominences as either radially or tangentially oriented structures. The quiet-Sun eruption has a predominately tangential structure in the slow-rise phase, but displays greater radial features during/after the eruption. The polar-swirl activation initially shows a strong radial contribution, which diminishes as more tangential structures appear during/after the activation. Our results demonstrate the successful application of the spatial RHT to prominences, leading to the classification of individual prominences and an insight into their dynamics.

arXiv comment22 Pages, 12 Figures, 5 Tables

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Cited by in corpus (1)

• A Robust Deep Learning Framework for Prominence Detection through Composite Feature Representations