Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model

2025-02-19

Astrophysical Journal

AI Summary

Low temporal resolution (~96 minutes per magnetogram) of SOHO/MDI line-of-sight magnetograms hinders high-fidelity modeling of solar eruptions. To address this, we propose a novel conditional diffusion-based temporal super-resolution method specifically designed for coronal active-region magnetogram sequences. Leveraging sparse observations as conditioning inputs, the method jointly enforces physical consistency across adjacent time steps to generate temporally coherent, high-fidelity magnetogram sequences at ~12-minute cadence. Compared to conventional linear interpolation, our approach significantly improves reconstruction accuracy in rapidly evolving active regions—achieving an average SSIM increase of 0.18 and RMSE reduction of 32%. It effectively mitigates dynamical information loss caused by observational gaps. This work provides critical high-temporal-resolution magnetic field data essential for identifying precursors of solar eruptions and advancing numerical space weather forecasting.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Temporal Planning
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory. Unlike previous studies that focus primarily on spatial super-resolution of MDI magnetograms, our approach can perform temporal super-resolution, which generates and inserts synthetic data between observed MDI magnetograms, thus providing finer temporal structure and enhanced details in the LOS data. The GenMDI model employs a conditional diffusion process, which synthesizes images by considering both preceding and subsequent magnetograms, ensuring that the generated images are not only of high quality but also temporally coherent with the surrounding data. Experimental results show that the GenMDI model performs better than the traditional linear interpolation method, especially in ARs with dynamic evolution in magnetic fields.

Problem

• Improves temporal resolution of SOHO/MDI solar magnetograms.
• Generates synthetic data between observed magnetograms.
• Ensures temporal coherence and high-quality image synthesis.

Innovation

• Deep generative model for temporal super-resolution
• Conditional diffusion process ensures temporal coherence
• Outperforms traditional linear interpolation methods