Assessing the Potential of Generative Models for Operational Solar Flare Forecasting

Score-based generative models (SBGMs) offer a path to mitigate class imbalance in solar flare prediction. Recent and prior work in the community has shown the ability of deep learning models to reliably separate flare-free intervals from flaring ones, but this framing potentially overestimates the performance when the objective is to discriminate strong (M-class+) flares from weaker events. In this regime of training only on intervals containing flares, the scarcity of high-quality examples of strong flares results in limited model performance for operational use. We study data augmentation for multichannel solar-flare images using synthetic strong-flare samples drawn from a conditional SBGM. In a simplified linear-regression setting, we derive conditions under which data augmentation can improve prediction. Using this as a reference, we outline potential conditions under which this can be applied for solar flare forecasting beyond what our empirical results show.

generative models

synthetic data augmentation

space weather