Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H$\alpha$ Images

Abstract

Solar filaments are prominent features in H$\alpha$ observations. During their evolution, they often exhibit complex morphological and topological changes, which, due to line-of-sight projection effects, appear as fragmentation and merging. These phenomena make it difficult to reliably track solar filaments using automated methods, as the same structure may appear different over time. To address this issue, we formulate filament fragment association as a graph-based learning problem, where individual fragments are represented as nodes and candidate pairwise relationships are modeled as edges to infer whether fragments belong to the same filament. Furthermore, we propose a novel graph neural network (GNN) that incorporates multiplicative aggregation layers to enhance feature aggregation in the graph, replacing conventional additive aggregation with a multiplicative mechanism to better capture information between fragments. Experimental results demonstrate that the proposed method achieves reliable performance in filament fragment association, validating the effectiveness of the graph-based formulation and the proposed multiplicative aggregation mechanism.

19 pages, 5 figures, 3 tables, accepted for publication in ApJS

References in corpus (14)

• Solar filament eruptions and their physical role in triggering Coronal Mass Ejections
• Some interesting topics provoked by the solar filament research in the past decade
• Statistical Analysis of Filament Features Based on the Hα Solar Images from 1988 to 2013 by Computer Automated Detection Method
• Development of an Advanced Automated Method for Solar Filament Recognition and Its Scientific Application to a Solar Cycle of MLSO Hα Data
• Formation of Compound Flux Rope by The Merging of Two Filament Channels, Associated Dynamics and its Stability
• Solar Filament Recognition Based on Deep Learning
• A Statistical Study of Solar Filament Eruptions That Forms High-Speed Coronal Mass Ejections
• A Universal Method for Solar Filament Detection from H-alpha Observations using Semi-supervised Deep Learning
• Automatic Detection of Limb Prominences in 304 A EUV Images
• Automated Detection of Filaments and their Disappearance using Full Disc H-alpha Images
• Initiation and propagation of coronal mass ejections
• SCSS-Net: Solar Corona Structures Segmentation by Deep Learning
• Interaction of solar jets with filaments: Triggering of large-amplitude filament oscillations
• Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk H$\alpha$ Images