Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
This work addresses the need for higher temporal resolution in solar magnetic field data for researchers in solar physics, though it is incremental as it builds on existing spatial super-resolution methods by focusing on temporal aspects.
The paper tackles the problem of low temporal resolution in SOHO/MDI magnetograms of solar active regions by developing a deep generative model called GenMDI, which generates synthetic data between observations to provide finer temporal structure and enhanced details, outperforming traditional linear interpolation, especially in dynamically evolving regions.
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 (SOHO). 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.