Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866929744251977728 |
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| author | Li, Jialiang Yurchyshyn, Vasyl Wang, Jason T. L. Wang, Haimin Abduallah, Yasser Alobaid, Khalid A. Xu, Chunhui Chen, Ruizhu Xu, Yan |
| author_facet | Li, Jialiang Yurchyshyn, Vasyl Wang, Jason T. L. Wang, Haimin Abduallah, Yasser Alobaid, Khalid A. Xu, Chunhui Chen, Ruizhu Xu, Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03959 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model Li, Jialiang Yurchyshyn, Vasyl Wang, Jason T. L. Wang, Haimin Abduallah, Yasser Alobaid, Khalid A. Xu, Chunhui Chen, Ruizhu Xu, Yan Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2503.03959 |