1. An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations.
- Author
-
Wang, R. C., Jorgensen, Anders M., Li, Dalin, Sun, Tianran, Yang, Zhen, and Peng, Xiaodong
- Subjects
MAGNETOSPHERIC physics ,SPACE environment ,OPTICAL flow ,MAGNETOSPHERE ,HELIOSPHERE ,SOLAR wind - 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. Plain Language Summary: New missions like SMILE and LEXI are set to provide detailed measurements using soft X‐ray imaging, offering insights into how the magnetosphere responds to solar wind. However, X‐ray observations with a short exposure time suffer from a low signal‐to‐noise ratio, while longer ones miss out on dynamic changes. This study introduces a neural network‐driven method to estimate magnetospheric dynamics using OMNI solar wind data and the expected SXI simulated observations. By combining models and observational data, this method improves upon traditional linear and optical flow interpolation techniques, offering better accuracy in capturing magnetospheric movements. The flexibility of this approach allows for its adaptation to various time intervals, making it a promising tool for studying magnetospheric dynamics in soft X‐rays. Key Points: A method is proposed for dynamically estimating changes in the Earth's magnetosheath using OMNI solar wind data and expected observationsWe use low‐resolution X‐ray images and high‐resolution solar wind data to estimate high‐time‐resolution X‐ray imagesThe method's flexibility enables easy extension to any interval (>3 min) for image estimation tasks, meeting diverse application needs [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF