An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations
Abstract Observations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW004040 |
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| author | R. C. Wang Anders M. Jorgensen Dalin Li Tianran Sun Zhen Yang Xiaodong Peng |
| author_facet | R. C. Wang Anders M. Jorgensen Dalin Li Tianran Sun Zhen Yang Xiaodong Peng |
| author_sort | R. C. Wang |
| collection | DOAJ |
| description | Abstract Observations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays. |
| format | Article |
| id | doaj-art-8284819e1f734ec5a63f0d8d1051794c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-8284819e1f734ec5a63f0d8d1051794c2025-01-14T16:31:08ZengWileySpace Weather1542-73902024-10-012210n/an/a10.1029/2024SW004040An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated ObservationsR. C. Wang0Anders M. Jorgensen1Dalin Li2Tianran Sun3Zhen Yang4Xiaodong Peng5National Space Science Center Chinese Academy of Sciences Beijing ChinaElectrical Engineering Department New Mexico Institute of Mining and Technology Socorro NM USANational Space Science Center Chinese Academy of Sciences Beijing ChinaNational Space Science Center Chinese Academy of Sciences Beijing ChinaNational Space Science Center Chinese Academy of Sciences Beijing ChinaNational Space Science Center Chinese Academy of Sciences Beijing ChinaAbstract Observations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.https://doi.org/10.1029/2024SW004040SMILELEXImagnetospheric physicssoft X‐ray imagermagnetosphere motion |
| spellingShingle | R. C. Wang Anders M. Jorgensen Dalin Li Tianran Sun Zhen Yang Xiaodong Peng An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations Space Weather SMILE LEXI magnetospheric physics soft X‐ray imager magnetosphere motion |
| title | An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations |
| title_full | An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations |
| title_fullStr | An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations |
| title_full_unstemmed | An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations |
| title_short | An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations |
| title_sort | adaptive x ray dynamic image estimation method based on omni solar wind parameters and sxi simulated observations |
| topic | SMILE LEXI magnetospheric physics soft X‐ray imager magnetosphere motion |
| url | https://doi.org/10.1029/2024SW004040 |
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