Shape-based Feature Engineering for Solar Flare Prediction

TL;DR

This paper introduces novel shape-based features derived from magnetogram images of the Sun using computational topology and geometry, demonstrating improved solar flare prediction when combined with traditional physics-based features.

Contribution

It presents a new set of shape-based features for solar flare prediction and shows they outperform traditional physics-based features in machine learning models.

Findings

Shape-based features outperform physics-based attributes.

Combining shape-based and physics-based features improves prediction accuracy.

Shape features extracted via computational topology enhance flare forecasting.

Abstract

Solar flares are caused by magnetic eruptions in active regions (ARs) on the surface of the sun. These events can have significant impacts on human activity, many of which can be mitigated with enough advance warning from good forecasts. To date, machine learning-based flare-prediction methods have employed physics-based attributes of the AR images as features; more recently, there has been some work that uses features deduced automatically by deep learning methods (such as convolutional neural networks). We describe a suite of novel shape-based features extracted from magnetogram images of the Sun using the tools of computational topology and computational geometry. We evaluate these features in the context of a multi-layer perceptron (MLP) neural network and compare their performance against the traditional physics-based attributes. We show that these abstract shape-based features outperform the features chosen by the human experts, and that a combination of the two feature sets improves the forecasting capability even further.