{
  "abstract": "We study equilibrium reconstruction for the Korea Superconducting Tokamak Advanced Research (KSTAR) device under magnetic sensor dropout, with a diffusion model as the prior. Magnetic measurements leave most components of the toroidal current density $J_ϕ$ undetermined, and sensor loss leaves more of them to the prior. The diffusion model learns only $J_ϕ$, conditioned on the coil currents, the plasma current and the product of major radius and toroidal field, which do not depend on the dropout. The physics enters through a linear forward operator built from sensor response matrices of the LIUQE code. Because the observations are linear, a variant of decoupled annealing posterior sampling fits them with a closed-form linear correction. On measured signals of 37 KSTAR shots, we switch off a random fraction (0.0 to 0.9, ten settings) of the 124 magnetic channels in use and compare with LIUQE under the same masks, taking the full-sensor LIUQE reconstruction as the label. Both methods fit the remaining channels to a similar level. The median relative distance of $J_ϕ$ from the label stays at 3.22--4.07\\% for the diffusion reconstruction in all settings, whereas that of LIUQE reaches 11.89\\% at dropout 0.9. At dropout 0.5--0.9, the diffusion reconstruction is closer to the label on 27--37 of the 37 shots. At low dropout, LIUQE is closer on most shots. For the derived flux, the diffusion reconstruction is closer on 21--36 shots at dropout 0.5--0.9, and its error at high dropout comes from the vessel current estimate, not from the prior.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36536",
  "author_total": 2,
  "authors": [
    {
      "name": "Hyungkeun Nam"
    },
    {
      "name": "Jaemin Seo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ckypzzhaxkyra.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36536",
  "fulltext_url": null,
  "id": "ckypzzhaxkyra",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36536",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3106|Magnetic confinement fusion research"
    ]
  ],
  "openalex": "W7214976848",
  "pdf_url": "https://arxiv.org/pdf/2609.36536",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ckypzzhaxkyra.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36536",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36536"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Diffusion prior for KSTAR equilibrium reconstruction under sensor dropout",
  "title_text": "Diffusion prior for KSTAR equilibrium reconstruction under sensor dropout",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/ckypzzhaxkyra",
  "venue": "arXiv",
  "versions": []
}