Incorporating polar field data for improved solar flare prediction

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 supplement local from active regions on photospheric magnetic of sun, provides global information predictor. While such features have been previously proposed for predicting next cycle’s intensity, in paper propose them help classify individual flares. We conduct experiments HMI employing four different algorithms that can exploit information. Additionally, a novel probabilistic mixture experts model simply effectively incorporate provide on-par state-of-the-art as Recurrent Neural Network (RNN). Our experimental results indicate usefulness prediction, which Heidke Skill Score (HSS2) by much 10.1% 1 .