The Impacts of Magnetogram Projection Effects on Solar Flare Forecasting

TL;DR

This study investigates how magnetogram projection effects influence machine learning solar flare forecasts, finding minimal performance improvements from correction methods and highlighting potential limitations of magnetogram data.

Contribution

It evaluates the impact of projection corrections on flare prediction accuracy and compares different mitigation strategies, revealing limited benefits from current correction techniques.

Findings

Data corrections slightly improve prediction rates.

Performance metrics show minimal change with advanced models.

Projection effects may have inherent limitations in flare forecasting.

Abstract

This work explores the impacts of magnetogram projection effects on machine learning-based solar flare forecasting models. Utilizing a methodology proposed by Falconer et al. (2016), we correct for projection effects present in Georgia State University's Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set. We then train and run a support vector machine classifier on the corrected and uncorrected data, comparing differences in performance. Additionally, we provide insight into several other methodologies that mitigate projection effects, such as stacking ensemble classifiers and active region location-informed models. Our analysis shows that data corrections slightly increase both the true positive (correctly predicted flaring samples) and false positive (non-flaring samples predicted as flaring) prediction rates, averaging a few percent. Similarly, changes in performance metrics are minimal for the stacking ensemble and location-based model. This suggests that a more complicated correction methodology may be needed to see improvements. It may also indicate inherent limitations when using magnetogram data for flare forecasting.