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Peer-reviewedCC BY 4.0The Astrophysical Journal Supplement Series

Multidomain Solar Extreme-ultraviolet Image Translation and Its Application to Differential Emission Measure Analysis

  • Mingyu Jeon1
  • Daeil Kim1
  • Junmu Youn1
  • Yong-Jae Moon1
  1. 1Kyung Hee University
Open at sourcePDFDOICite

Abstract

We develop five multidomain models for solar extreme-ultraviolet (EUV) image translation, investigate their dependence on input data and solar cycle phase, and apply them to differential emission measure (DEM) analysis. Using Solar Dynamics Observatory (SDO) data from 2011 to 2025, our models generate the six optically thin Atmospheric Imaging Assembly (AIA) EUV images from five input configurations using an optically thin EUV image, a magnetogram (MAG), and a 304 Å image: EUV-only, EUV+MAG, EUV+304, MAG-only, and 304-only. They achieve performance comparable to one-to-one models while reducing the required number of models from 102 to 5. The overall performance ranking is EUV+304 > EUV+MAG > EUV-only > 304-only > MAG-only, with average correlation coefficient values of 0.93, 0.92, 0.91, 0.89, and 0.78, respectively. The ranking is preserved across solar cycle phases, although all configurations perform better during the rising and declining phases than during the minimum and maximum phases. These results show that our EUV+304 model achieves the best overall performance and provides data-driven support for this channel combination in spaceborne observatories. Using SDO/AIA observations, we confirm that AI-generated EUV images can be used for DEM analysis. We then apply our models to limited-channel observations from the Solar and Heliospheric Observatory, STEREO-A, and Solar Orbiter. DEMs derived with AI-generated EUV images are closer to the six-channel SDO/AIA reference DEM than those derived from the limited channels alone. We further show that our models can improve DEM analysis of pre-SDO observations, as evaluated against Hinode/X-Ray Telescope observations. We expect that our multidomain models could be effectively used for DEM analysis of limited-channel observations.

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Cite

BibTeXRIS
BibTeX
@article{jeon2026multidomain,
  title = {{Multidomain Solar Extreme-ultraviolet Image Translation and Its Application to Differential Emission Measure Analysis}},
  author = {Mingyu Jeon and Daeil Kim and Junmu Youn and Yong-Jae Moon},
  journal = {The Astrophysical Journal Supplement Series},
  year = {2026},
  month = oct,
  doi = {10.3847/1538-4365/aea40d},
  url = {https://iopscience.iop.org/article/10.3847/1538-4365/aea40d},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multidomain Solar Extreme-ultraviolet Image Translation and Its Application to Differential Emission Measure Analysis
AU  - Mingyu Jeon
AU  - Daeil Kim
AU  - Junmu Youn
AU  - Yong-Jae Moon
PY  - 2026
DA  - 2026/10/13
JO  - The Astrophysical Journal Supplement Series
DO  - 10.3847/1538-4365/aea40d
UR  - https://iopscience.iop.org/article/10.3847/1538-4365/aea40d
AB  - We develop five multidomain models for solar extreme-ultraviolet (EUV) image translation, investigate their dependence on input data and solar cycle phase, and apply them to differential emission measure (DEM) analysis. Using Solar Dynamics Observatory (SDO) data from 2011 to 2025, our models generate the six optically thin Atmospheric Imaging Assembly (AIA) EUV images from five input configurations using an optically thin EUV image, a magnetogram (MAG), and a 304 Å image: EUV-only, EUV+MAG, EUV+304, MAG-only, and 304-only. They achieve performance comparable to one-to-one models while reducing the required number of models from 102 to 5. The overall performance ranking is EUV+304 > EUV+MAG > EUV-only > 304-only > MAG-only, with average correlation coefficient values of 0.93, 0.92, 0.91, 0.89, and 0.78, respectively. The ranking is preserved across solar cycle phases, although all configurations perform better during the rising and declining phases than during the minimum and maximum phases. These results show that our EUV+304 model achieves the best overall performance and provides data-driven support for this channel combination in spaceborne observatories. Using SDO/AIA observations, we confirm that AI-generated EUV images can be used for DEM analysis. We then apply our models to limited-channel observations from the Solar and Heliospheric Observatory, STEREO-A, and Solar Orbiter. DEMs derived with AI-generated EUV images are closer to the six-channel SDO/AIA reference DEM than those derived from the limited channels alone. We further show that our models can improve DEM analysis of pre-SDO observations, as evaluated against Hinode/X-Ray Telescope observations. We expect that our multidomain models could be effectively used for DEM analysis of limited-channel observations.
SN  - 0067-0049
SN  - 1538-4365
ER  -