{
  "abstract": "Magnetic reconnection is a fundamental plasma process that plays a crucial role in energy dissipation in plasma turbulence, in which magnetic energy is rapidly converted into particle energy. However, the identification of reconnection within turbulence remains a significant challenge. To automatically detect magnetic reconnection in plasma turbulence, we propose a novel and efficient classifier called a dual-branch lightweight hybrid convolutional neural network (DHCNN), which, for the first time, achieves automatic localization of reconnection sites by effectively leveraging negative-valued information in physical variables. We adopt a temporal partitioning scheme together with diverse data augmentation strategies to enhance the model’s generalization ability. Our results show that the DHCNN accurately identifies reconnection sites, achieving a Matthews correlation coefficient ( M ) of 0.67, a true skill statistic ( T ) of 0.67, a recall ( R ) of 0.83, a specificity ( S ) of 0.84, and an accuracy ( A ) of 0.84. Finally, by analyzing the impact of different physical variables, we find that the total current density (| J |), in-plane components of the magnetic field ( B x , B z ), and energy conversion term ( D e ) are the primary drivers of the model’s performance.",
  "abstract_shown": true,
  "affiliations": [
    "Wuhan University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Y. Yin"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Y. Huang",
      "orcid": "0000-0002-3595-2525"
    },
    {
      "affs": [
        0
      ],
      "name": "Q. Y. Xiong",
      "orcid": "0000-0003-1840-3281"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Z. G. Yuan",
      "orcid": "0000-0002-3756-3507"
    },
    {
      "affs": [
        0
      ],
      "name": "K. Jiang",
      "orcid": "0000-0001-7889-0507"
    },
    {
      "affs": [
        0
      ],
      "name": "H. H. Wu",
      "orcid": "0000-0003-0424-9228"
    },
    {
      "affs": [
        0
      ],
      "name": "H. W. Zhan",
      "orcid": "0009-0002-4006-6162"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. T. Tang"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Bai",
      "orcid": "0009-0001-9831-8871"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fcretf87cagty.bib",
  "cited_by": 0,
  "doi": "10.3847/1538-4365/aeaa32",
  "fulltext_url": null,
  "id": "fcretf87cagty",
  "issns": [
    "0067-0049",
    "1538-4365"
  ],
  "landing_url": "https://iopscience.iop.org/article/10.3847/1538-4365/aeaa32",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://iopscience.iop.org/article/10.3847/1538-4365/aeaa32/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "American Astronomical Society",
  "ris": "https://research.draconitic.com/paper/fcretf87cagty.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:15Z",
      "native_id": "10.3847/1538-4365/aeaa32",
      "source": "crossref",
      "url": "https://iopscience.iop.org/article/10.3847/1538-4365/aeaa32"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "journal-openalex:3103|Stellar, planetary, and galactic studies",
      "subfield": "stellar"
    }
  ],
  "title": "Identification of Magnetic Reconnection in Plasma Turbulence Using a Dual-branch Lightweight Hybrid Convolutional Neural Network",
  "title_text": "Identification of Magnetic Reconnection in Plasma Turbulence Using a Dual-branch Lightweight Hybrid Convolutional Neural Network",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T00:20:43Z",
  "url": "https://research.draconitic.com/paper/fcretf87cagty",
  "venue": "The Astrophysical Journal Supplement Series",
  "versions": []
}