04: Leveraging Generative Models for Forecasting Solar Flares

Music City Center 

Class imbalance is a common challenge in datasets where one category significantly outnumbers the others. This issue is particularly relevant in the prediction of extreme tail events, where the occurrence of such events is vastly outweighed by their non-occurrence, especially in scientific data. Generative learning models help address this issue by enabling users to generate synthetic samples from the learned joint distribution to augment the training data.

In this project, we train diffusion models on space weather data to mitigate class imbalance. Specifically, we train a diffusion model on multi-channel images captured by the Atmospheric Imaging Assembly (AIA) and the Helioseismic Magnetic Imager (HMI) aboard the Solar Dynamics Observatory (SDO). These images focus on active regions six hours before reaching a peak flare time. We assess the fidelity and quality of the generated synthetic images and evaluate their effectiveness in improving flare forecasting using common techniques and models from the space weather community for solar flare prediction.

Diffusion Models

Data Augmentation

Space Weather 

Astrostatistics Interest Group