Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data
This addresses data scarcity for solar flare prediction, but it is incremental as it applies an existing generative method to a new domain-specific dataset.
The study tackled the problem of insufficient data for training machine learning models in solar activity forecasting by using a Denoising Diffusion Probabilistic Model (DDPM) to generate synthetic solar images, achieving promising results as evaluated by metrics like FID and F1-score.
Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient data. This study proposes using generative deep learning models, specifically a Denoising Diffusion Probabilistic Model (DDPM), to create synthetic images of solar phenomena, including flares of varying intensities. By employing a dataset from the AIA instrument aboard the SDO spacecraft, focusing on the 171 Å band that captures various solar activities, and classifying images with GOES X-ray measurements based on flare intensity, we aim to address the data scarcity issue. The DDPM's performance is evaluated using cluster metrics, Frechet Inception Distance (FID), and F1-score, showcasing promising results in generating realistic solar imagery. We conduct two experiments: one to train a supervised classifier for event identification and another for basic flare prediction, demonstrating the value of synthetic data in managing imbalanced datasets. This research underscores the potential of DDPMs in solar data analysis and forecasting, suggesting further exploration into their capabilities for solar flare prediction and application in other deep learning and physical tasks.