Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation
The accurate forecasting of solar flares is considered a key goal within the solar physics and space weather communities. There is significant potential for flare prediction to be improved by incorporating topological fluxes of magnetogram data sets, without the need to invoke three-dimensional magn...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/ada600 |
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| Summary: | The accurate forecasting of solar flares is considered a key goal within the solar physics and space weather communities. There is significant potential for flare prediction to be improved by incorporating topological fluxes of magnetogram data sets, without the need to invoke three-dimensional magnetic field extrapolations. Topological quantities such as magnetic helicity and magnetic winding have shown significant potential toward this aim, and provide spatiotemporal information about the complexity of active region magnetic fields. This study develops time series that are derived from the spatial fluxes of helicity and winding that show significant potential for solar flare prediction. It is demonstrated that time-series signals, which correlate with flare onset times, also exhibit clear spatial correlations with eruptive activity, establishing a potential causal relationship. A significant database of helicity and winding fluxes and associated time series across 144 active regions is generated using Space-Weather HMI Active Region Patches data processed with the Active Region Topology (or ARTop) code that forms the basis of the time-series and spatial investigations conducted here. We find that a number of time series in this data set often exhibit extremal signals that occur 1–8 hr before a flare. This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms. |
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| ISSN: | 1538-4357 |