An automated detection method applied to simulated flare ribbon data identifies fine structures whose motions and flux distribution are consistent with plasmoid-mediated reconnection.
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2 Pith papers cite this work. Polarity classification is still indexing.
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astro-ph.SR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A deep learning model with dynamic masks and multi-parameter constraints predicts solar vector magnetic fields over 12 hours, achieving SSIM 0.912 and CC 0.998 for the radial component with 7.82% unsigned flux error.
citing papers explorer
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Automatic detection of Flare Ribbon Fine Structures as Proxies for Plasmoid Dynamics in Flare Reconnection
An automated detection method applied to simulated flare ribbon data identifies fine structures whose motions and flux distribution are consistent with plasmoid-mediated reconnection.
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Deep Learning with Magnetic Parameter Constraints for Short-Term Prediction of Solar Active Region Vector Magnetic Fields
A deep learning model with dynamic masks and multi-parameter constraints predicts solar vector magnetic fields over 12 hours, achieving SSIM 0.912 and CC 0.998 for the radial component with 7.82% unsigned flux error.