{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.07433",
  "author_total": 8,
  "authors": [
    {
      "name": "Cheng Zhong"
    },
    {
      "name": "Zhongwei Yang"
    },
    {
      "name": "Tianran Sun"
    },
    {
      "name": "Chengyan Huang"
    },
    {
      "name": "Jiajia Liu"
    },
    {
      "name": "Jingnan Guo"
    },
    {
      "name": "Yuming Wang"
    },
    {
      "name": "Chi Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/725wgwxwbww58.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.07433",
  "fulltext_url": null,
  "id": "725wgwxwbww58",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.07433",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "astro-ph.SR"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.07433",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/725wgwxwbww58.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:30Z",
      "native_id": "2611.07433",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.07433"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.SR",
      "subfield": "stellar"
    }
  ],
  "title": "MP-DualNet: A Dual-Branch Deep Learning Approach for Magnetopause Boundary Detection Using Simulated SXI Images",
  "title_text": "MP-DualNet: A Dual-Branch Deep Learning Approach for Magnetopause Boundary Detection Using Simulated SXI Images",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:41Z",
  "url": "https://research.draconitic.com/paper/725wgwxwbww58",
  "venue": "arXiv",
  "versions": []
}