Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. II. XGBoost Model

TL;DR

This study develops a machine learning model using topologically derived magnetic parameters to predict solar flares, achieving high accuracy and interpretability, and highlights the importance of magnetic topology in flare forecasting.

Contribution

The paper introduces a novel flare prediction model combining topological magnetic parameters with XGBoost, demonstrating improved performance and physical interpretability over previous methods.

Findings

Model achieves TSS of 0.804 on validation set.

Magnetic topology features are highly predictive.

Projection effects impact model performance near the solar limb.

Abstract

Solar flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived photospheric magnetic parameters. We employ the \texttt{ARTop} framework to compute the time-dependent input rates of magnetic winding and helicity across more than $10^5$ active region (AR) observations, decomposing them into current-carrying and potential components to reduce sensitivity to optical flow methods. An \texttt{XGBoost} machine learning model is trained on these topological time series, alongside engineered features including rolling statistics, kurtosis, and flare history, to predict the probability of $\geq$M1.0-class flares within the next 24 hours. The model demonstrates strong performance on a validation set, with a True Skill Statistic (TSS) of 0.804 for once daily operational region forecasts. When applied to a fully independent holdout set, the operational forecast achieves a TSS of \tsssa. A SHapley Additive exPlanations (SHAP) analysis confirms the model's physical interpretability, identifying flare history and accumulated current-carrying winding and helicity as the most important features. The main challenges identified are false positives arising from ARs with frequent C-class flaring and systematic errors introduced by projection effects when ARs are near the limb. Excluding limb-affected data yields no improvement in the holdout set TSS (\TSSalert\ versus \tsssa), due to the overall decreased number of flares. However, our per-region analysis indicates that mitigating these projection effects is crucial for future operational deployment. This work establishes magnetic topology, particularly its current-carrying components, as a highly effective and physically meaningful set of predictors for solar flare forecasting.