Predictive Capabilities of Avalanche Models for Solar Flares

Abstract

We assess the predictive capabilities of various classes of avalanche models for solar flares. We demonstrate that avalanche models cannot generally be used to predict specific events due to their high sensitivity to their embedded stochastic process. We show that deterministically driven models can nevertheless alleviate this caveat and be efficiently used for large events predictions. Our results promote a new approach for large (typically X-class) solar flares predictions based on simple and computationally inexpensive avalanche models.

arXiv comment16 pages, 7 figures, 1 table, accepted for publication in Solar Physics

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• Flaring together: A preferred angular separation between sympathetic flares on the Sun
• Recent Developments in Helioseismic Analysis Methods and Solar Data Assimilation
• Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
• Energy Definition and Minimization in Avalanche Models for Solar Flares
• The Flare Likelihood and Region Eruption Forecasting (FLARECAST) Project: Flare forecasting in the big data & machine learning era
• Forecasting Solar Flares by Data Assimilation in Sandpile Models