MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams

2026-09-30

AI Summary

This study addresses the challenge of inferring solar photospheric vector magnetic fields from ultraviolet and extreme-ultraviolet images lacking polarization information. To overcome this limitation, we propose a multi-wavelength image processing framework based on denoising diffusion probabilistic models. Leveraging SDO/AIA data, the method generates and disambiguates high-precision vector magnetograms comparable to Hinode/SOT quality in an end-to-end manner. This work represents the first direct mapping from unpolarized intensity observations to vector magnetic fields, demonstrating robust generalization across solar cycles. The model accurately reproduces ground-truth measurements and active region structures, while remaining amenable to fine-tuning for adaptation to other observational instruments such as STEREO and GOES-R.

Abstract

Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by inverting and disambiguating the full Stokes vector at multiple passbands, which is demanding. Here, we investigate how well we can estimate photospheric vector magnetograms from UV/EUV filtergrams. This problem is challenging and intrinsically ambiguous without polarization information, as the mapping from UV/EUV intensity to the magnetic field is indirect and ill-posed. We introduce MAGiDiff, a machine-learning-based method that uses denoising diffusion models to estimate vector magnetograms from UV/EUV filtergrams. As input, MAGiDiff takes a stack of filtergrams from the Solar Dynamics Observatory (SDO) / Atmospheric Imaging Assembly (AIA); as output, it is trained to estimate the disambiguated vector magnetogram as seen by Hinode / Solar Optical Telescope-Spectro-Polarimeter (SOT-SP). We show that MAGiDiff can accurately mimic the Hinode ground-truth. Additionally, we probe MAGiDiff's understanding of the physical structure and magnetic connectivity. On full-disk, we show that it produces plausible structures for active regions. MAGiDiff generalizes across solar cycles despite hemispheric polarity reversal, and can be fine-tuned to other EUV instruments including STEREO/EUVI and GOES-R/SUVI. While clearly not a substitute for a dedicated instrument, MAGiDiff opens the door to new capabilities.

Problem

• photospheric vector magnetic field
• UV/EUV filtergrams
• vector magnetogram estimation
• ill-posed problem

Innovation

• Denoising Diffusion Models
• Vector Magnetograms
• UV/EUV Filtergrams
• Cross-cycle Generalization
• Machine Learning