Incorporating Polar Field Data for Improved Solar Flare Prediction

TL;DR

This paper demonstrates that incorporating polar magnetic field data into machine learning models enhances solar flare prediction accuracy, achieving significant improvements over existing methods.

Contribution

It introduces a novel approach to include polar field data in flare prediction models and proposes a probabilistic mixture of experts model for effective integration.

Findings

Polar field data improves prediction accuracy by up to 10.1% HSS2.

The proposed mixture of experts model performs on par with state-of-the-art algorithms.

Global polar data provides valuable information for classifying individual solar flares.

Abstract

In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle's intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.