Optimal homogeneous extreme-event predictors under regular variation are given by quantiles of a tilted angular measure and admit universally consistent peaks-over-threshold estimators.
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12 Pith papers cite this work, alongside 740 external citations. Polarity classification is still indexing.
12
Pith papers citing it
740
external citations · OpenAlex
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2026 12roles
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A multi-height extrapolation framework using chromospheric vector data recovers a pre-eruptive flux-rope configuration in an observed solar filament, outperforming photosphere-only models in simulation tests.
Gaussian separation reveals that optimization and CFIT NLFFF models reproduce some coronal current signatures from magnetogram data for AR 11429 but the CFIT model alters a flux rope signature along the lower PIL.
FOXES is a Vision Transformer framework that predicts solar soft X-ray irradiance from EUV observations with 0.051 dex mean absolute error while providing spatial attribution of emission sources.
Non-force-free initial magnetic fields in solar flare simulations release about twice the magnetic energy and yield EUV emission closer to observations than conventional NLFF extrapolations.
Hybrid neural network predicts eruptive versus confined solar flares from SDO/HMI magnetogram sequences, reports good performance, and links results to magnetic flux cancellation in polarity inversion lines.
Solar active regions' compliance with Hale's, Joy's, and helicity laws does not determine flare productivity; flux and size thresholds do.
TMF simulation of AR 12975 reproduces filament channel formation and energy/helicity injection but shows eruption helicity ratio of 0.23 and torus instability at 0.32 due to complex field configuration.
Deep learning on magnetic field features predicts solar flares, with SHAP values and PDPs added to reveal feature importance and trends.
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.
The paper describes swdatatoolkit, a modular Python library that consolidates data acquisition, image processing, edge detection, texture analysis, magnetic field handling, and higher-level parameter derivation for solar and space weather research.
Sympathetic filament and active-region eruptions produced two overlapping CMEs whose interaction compressed southward magnetic fields, driving a major geomagnetic storm with Dst ~ -333 nT.
citing papers explorer
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On the optimal prediction of extreme events
Optimal homogeneous extreme-event predictors under regular variation are given by quantiles of a tilted angular measure and admit universally consistent peaks-over-threshold estimators.
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Chromospheric magnetic field extrapolations reveal the flux-rope configuration of a solar filament
A multi-height extrapolation framework using chromospheric vector data recovers a pre-eruptive flux-rope configuration in an observed solar filament, outperforming photosphere-only models in simulation tests.
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Validating Coronal Magnetic Field Models Using Gaussian Separation
Gaussian separation reveals that optimization and CFIT NLFFF models reproduce some coronal current signatures from magnetogram data for AR 11429 but the CFIT model alters a flux rope signature along the lower PIL.
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Improving Solar Flare Soft X-ray Classification With FOXES: A Framework For Operational X-ray Emission Synthesis
FOXES is a Vision Transformer framework that predicts solar soft X-ray irradiance from EUV observations with 0.051 dex mean absolute error while providing spatial attribution of emission sources.
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Energetics and Emission in a Simulated Solar Flare Initialised by a Non-Force Free Magnetic Field
Non-force-free initial magnetic fields in solar flare simulations release about twice the magnetic energy and yield EUV emission closer to observations than conventional NLFF extrapolations.
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Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network
Hybrid neural network predicts eruptive versus confined solar flares from SDO/HMI magnetogram sequences, reports good performance, and links results to magnetic flux cancellation in polarity inversion lines.
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Investigation on the Relation between Active Regions' Compliance with Empirical Laws and Flare Productivity
Solar active regions' compliance with Hale's, Joy's, and helicity laws does not determine flare productivity; flux and size thresholds do.
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Formation and Eruption of Filament Channel in Solar Active Region 12975: Insights from Observations and Simulations of Magnetic Field Evolution
TMF simulation of AR 12975 reproduces filament channel formation and energy/helicity injection but shows eruption helicity ratio of 0.23 and torus instability at 0.32 due to complex field configuration.
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Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning
Deep learning on magnetic field features predicts solar flares, with SHAP values and PDPs added to reveal feature importance and trends.
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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.
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Describing the swdatatoolkit: A Space Weather Data Analysis Library
The paper describes swdatatoolkit, a modular Python library that consolidates data acquisition, image processing, edge detection, texture analysis, magnetic field handling, and higher-level parameter derivation for solar and space weather research.
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A Major Geomagnetic Storm in 2024 October Linked to Sympathetic CME--Prominence Eruptions
Sympathetic filament and active-region eruptions produced two overlapping CMEs whose interaction compressed southward magnetic fields, driving a major geomagnetic storm with Dst ~ -333 nT.