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Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator

arXiv 2606.08652 ↗First posted 7 Jun 2026astro-ph.SR cs.AI cs.CVcited by 0

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Abstract (arXiv)

Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO. To extend EUV coronal context into earlier periods, we leverage the multi-decade availability of full-disk HeI observations, whose absorption is modulated by coronal irradiance and magnetic topology and is widely used as a proxy for open-field regions. We present a diffusion-based conditional image translation framework, Coronal Hole-aware Diffusion Model Translator (CH-aware DMT), to reconstruct synthetic SDO/AIA 193 AA EUV images from HeI inputs. The model is trained on temporally co-aligned SOLIS HeI and AIA 193 AA pairs spanning 2011--2015 using a month-based split, where January--October are used for training, November is used for validation, and December for testing. On the held-out test set, the reconstructions preserve dominant full-disk EUV morphology (CC=0.92) and recover CH-related low-intensity structure (CC=0.84). We further assess historical applicability by (1) comparing reconstructed AIA 193 AA morphology with SOHO/EIT 195 AA over 2005--2015; (2) comparing reconstructed AIA 193 AA images generated from KPVT HeI inputs against Yohkoh/SXT soft X-ray observations; and (3) evaluating long-term reconstructed disk-integrated emission statistics against observational EUV series and independent solar activity proxies (sunspot number and F10.7 radio flux over 1974--2015). These results indicate that CH-aware DMT conditioned on HeI can provide a physically plausible synthetic AIA 193 AA coronal proxy for historical studies, supporting multi-decade analyses of large-scale coronal evolution before the direct EUV imaging was available.

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References · 6

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  3. 2021High Resolution Solar Image Generation using Generative Adversarial Networkscited by 6
  4. 2022Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translationcited by 6
  5. 2024Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA datacited by 4
  6. 2020Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learningcited by 3

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  6. 2025BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation
  7. 2026FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
  8. 2023Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics