Speaker
Description
The largest solar flares, of class X and above, are associated with strong energetic particle acceleration. The reconnection process thought to be responsible for solar flares can be mimicked with so-called cellular automata. In particular, sandpile models have proven to well reproduce solar flare statistics (Charbonneau et al. 2001) and have recently been shown to be consistent with MHD simulations of the low plasma-beta regions of the solar atmosphere (Lamarre et al. in review).
To begin, we describe our flare prediction pipeline, coupling the GOES X-ray flux to a new sandpile model with strong predictive capabilities (Strugarek et al. 2014) and improved solar-like statistical properties (Lamarre et al. in prep). Then, we report on integrating machine learning to our pipeline to speed up the assimilation process and render our approach closer to a real-time prediction tool. Finally, we give an update on the skill scores of this technique, from its initial promising results (Thibeault et al. 2022), to a new larger and more realistic sample of solar flare events.
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