Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution

TL;DR

This paper presents a novel deep learning approach using Denoising Diffusion Probabilistic Models to predict solar magnetogram evolution, improving physical accuracy and structural integrity over traditional methods.

Contribution

Introduces a new generative model for solar magnetogram forecasting that combines image quality and physical metrics, advancing solar physics analysis.

Findings

DDPMs effectively preserve magnetic structures and flux.

Outperforms traditional persistence models in accuracy.

Applicable to flaring and non-flaring solar conditions.

Abstract

Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduce a novel method to predict the evolution of the solar line-of-sight (LoS) magnetogram using image-to-image translation with Denoising Diffusion Probabilistic Models (DDPMs). Our approach combines "computer science metrics" for image quality and "physics metrics" for physical accuracy to evaluate model performance. The results indicate that DDPMs are effective in maintaining the structural integrity, the dynamic range of solar magnetic fields, the magnetic flux and other physical features such as the size of the active regions, surpassing traditional persistence models, also in flaring situation. We aim to use deep learning not only for visualisation but as an integrative and interactive tool for telescopes, enhancing our understanding of unexpected physical events like solar flares. Future studies will aim to integrate more diverse solar data to refine the accuracy and applicability of our generative model.