Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
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 o...
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2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adb032 |
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| author | Jialiang Li Vasyl Yurchyshyn Jason T. L. Wang Haimin Wang Yasser Abduallah Khalid A. Alobaid Chunhui Xu Ruizhu Chen Yan Xu |
| author_facet | Jialiang Li Vasyl Yurchyshyn Jason T. L. Wang Haimin Wang Yasser Abduallah Khalid A. Alobaid Chunhui Xu Ruizhu Chen Yan Xu |
| author_sort | Jialiang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-70d78c82a6d14c5f86c9db5ffcc75900 |
| institution | DOAJ |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-70d78c82a6d14c5f86c9db5ffcc759002025-08-20T02:43:49ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01980222810.3847/1538-4357/adb032Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative ModelJialiang Li0Vasyl Yurchyshyn1https://orcid.org/0000-0001-9982-2175Jason T. L. Wang2https://orcid.org/0000-0002-2486-1097Haimin Wang3https://orcid.org/0000-0002-5233-565XYasser Abduallah4https://orcid.org/0000-0003-0792-2270Khalid A. Alobaid5Chunhui Xu6Ruizhu Chen7https://orcid.org/0000-0002-2632-130XYan Xu8Institute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; Department of Computer Science, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USABig Bear Solar Observatory, New Jersey Institute of Technology , 40386 North Shore Lane, Big Bear City, CA 92314, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; Department of Computer Science, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; Big Bear Solar Observatory, New Jersey Institute of Technology , 40386 North Shore Lane, Big Bear City, CA 92314, USA; Center for Solar-Terrestrial Research, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; Department of Computer Science, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; College of Applied Computer Sciences, King Saud University , Riyadh 11451, Saudi ArabiaInstitute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; Department of Computer Science, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USAW. W. Hansen Experimental Physics Laboratory, Stanford University , Stanford, CA 94305, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USA ; wangj@njit.edu, haimin.wang@njit.edu; Big Bear Solar Observatory, New Jersey Institute of Technology , 40386 North Shore Lane, Big Bear City, CA 92314, USA; Center for Solar-Terrestrial Research, New Jersey Institute of Technology , University Heights, Newark, NJ 07102, USAWe 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.https://doi.org/10.3847/1538-4357/adb032Solar active regionsSolar magnetic fieldsThe SunSolar physics |
| spellingShingle | Jialiang Li Vasyl Yurchyshyn Jason T. L. Wang Haimin Wang Yasser Abduallah Khalid A. Alobaid Chunhui Xu Ruizhu Chen Yan Xu Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model The Astrophysical Journal Solar active regions Solar magnetic fields The Sun Solar physics |
| title | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model |
| title_full | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model |
| title_fullStr | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model |
| title_full_unstemmed | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model |
| title_short | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model |
| title_sort | improving the temporal resolution of soho mdi magnetograms of solar active regions using a deep generative model |
| topic | Solar active regions Solar magnetic fields The Sun Solar physics |
| url | https://doi.org/10.3847/1538-4357/adb032 |
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