MP-DualNet: A Dual-Branch Deep Learning Approach for Magnetopause Boundary Detection Using Simulated SXI Images
Abstract
The Earth's magnetopause is a key boundary governing solar wind-magnetosphere coupling. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will provide global soft X-ray imaging of the magnetosphere through solar wind charge exchange (SWCX) emission, offering a new opportunity for remote sensing of magnetopause dynamics. However, the limited field of view (FOV) and variable observation geometry of the Soft X-ray Imager (SXI) pose challenges for reliable magnetopause detection. In this study, we develop MP-DualNet, an end-to-end dual-branch deep learning framework for automated magnetopause detection from simulated SXI observations. The framework jointly performs coarse localization to determine magnetopause visibility and relative position within the FOV, and fine-scale boundary localization through pixel-level segmentation. The model is trained and evaluated using line-of-sight magnetopause envelope maps derived from global hybrid simulations under different solar wind and interplanetary magnetic field conditions. The results demonstrate stable and consistent performance, with a classification accuracy exceeding 0.94 and a segmentation IoU above 0.97. For the test set, the mean absolute angular displacement from the reference boundary was 0.488 degrees. The model maintains robust performance across a wide range of noise levels and generalizes well, as demonstrated by its application to an independent storm event. These results indicate that MP-DualNet provides a reliable and generalizable approach for magnetopause detection in SXI observations, supporting future 3D reconstruction and space weather studies in the context of the SMILE mission.
Related
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BibTeX
@misc{zhong2026mpdualnet,
title = {{MP-DualNet: A Dual-Branch Deep Learning Approach for Magnetopause Boundary Detection Using Simulated SXI Images}},
author = {Cheng Zhong and Zhongwei Yang and Tianran Sun and Chengyan Huang and Jiajia Liu and Jingnan Guo and Yuming Wang and Chi Wang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.07433},
eprint = {2611.07433},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.07433},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - MP-DualNet: A Dual-Branch Deep Learning Approach for Magnetopause Boundary Detection Using Simulated SXI Images
AU - Cheng Zhong
AU - Zhongwei Yang
AU - Tianran Sun
AU - Chengyan Huang
AU - Jiajia Liu
AU - Jingnan Guo
AU - Yuming Wang
AU - Chi Wang
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.07433
UR - https://arxiv.org/abs/2611.07433
AB - The Earth's magnetopause is a key boundary governing solar wind-magnetosphere coupling. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will provide global soft X-ray imaging of the magnetosphere through solar wind charge exchange (SWCX) emission, offering a new opportunity for remote sensing of magnetopause dynamics. However, the limited field of view (FOV) and variable observation geometry of the Soft X-ray Imager (SXI) pose challenges for reliable magnetopause detection. In this study, we develop MP-DualNet, an end-to-end dual-branch deep learning framework for automated magnetopause detection from simulated SXI observations. The framework jointly performs coarse localization to determine magnetopause visibility and relative position within the FOV, and fine-scale boundary localization through pixel-level segmentation. The model is trained and evaluated using line-of-sight magnetopause envelope maps derived from global hybrid simulations under different solar wind and interplanetary magnetic field conditions. The results demonstrate stable and consistent performance, with a classification accuracy exceeding 0.94 and a segmentation IoU above 0.97. For the test set, the mean absolute angular displacement from the reference boundary was 0.488 degrees. The model maintains robust performance across a wide range of noise levels and generalizes well, as demonstrated by its application to an independent storm event. These results indicate that MP-DualNet provides a reliable and generalizable approach for magnetopause detection in SXI observations, supporting future 3D reconstruction and space weather studies in the context of the SMILE mission.
ER -