An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions

TL;DR

This paper uses an interpretable machine learning approach, specifically a random forest model, to analyze and understand the features of solar active regions that influence the likelihood of solar flares, aiding space weather prediction.

Contribution

It introduces an interpretable RF-based method to identify key physical features of active regions that predict solar flares, enhancing understanding and prediction accuracy.

Findings

AR_Type_Today is a highly influential feature.

Hale_Class_Yesterday has minimal impact.

NoS_Difference significantly affects model decisions.

Abstract

Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. Solar flares and associated coronal mass ejections are sources of space weather that adversely impact devices at or near Earth, including the obstruction of high-frequency radio waves utilized for communication and the deterioration of power grid operations. Tracking and delivering early and precise predictions of solar flares is essential for readiness and catastrophe risk mitigation. This paper employs the random forest (RF) model to address the binary classification task, analyzing the links between solar flares and their originating ARs with observational data gathered from 2011 to 2021 by SolarMonitor.org and the XRT flare database. We seek to identify the physical features of a source AR that significantly influence its potential to trigger >=C-class flares. We found that the features of AR_Type_Today, Hale_Class_Yesterday are the most and the least prepotent features, respectively. NoS_Difference has a remarkable effect in decision-making in both global and local interpretations.