{
  "abstract": "Loss-cone boundary determination in tokamaks is cast as an active learning problem in which a Bayesian logistic regression model with radial basis function features acts as the learner and the guiding-center orbit integrator acts as the labeling method. The approach is applied to an analytic tokamak equilibrium across a mesh of launch positions, producing a database of loss-probability models with approximate posterior uncertainty, each generated within a prescribed trajectory budget. Once generated, the database supports downstream applications, illustrated here with three examples. First, integrating the probability models against a Maxwellian distribution yields the lost-ion fraction and the energy and parallel momentum carried by lost ions, with posterior uncertainty propagated analytically to each integral. Second, a steady balance between the resulting orbit-loss torque and radial angular-momentum diffusion gives co-current edge rotation whose amplitude is set mainly by the prescribed momentum diffusivity and loss-region renewal rate. Third, a neural network surrogate trained on the database learns the in-domain mapping from launch position to loss-probability image, and transport integrals computed from its predictions closely reproduce the Bayesian reference values on held-out samples from the same mesh.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36123",
  "author_total": 4,
  "authors": [
    {
      "name": "Omar E. Lopez"
    },
    {
      "name": "Minglei Yang"
    },
    {
      "name": "Mark Cianciosa"
    },
    {
      "name": "Gary M. Staebler"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e4c25808pr0r9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36123",
  "fulltext_url": null,
  "id": "e4c25808pr0r9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36123",
  "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": "W7214962981",
  "pdf_url": "https://arxiv.org/pdf/2609.36123",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e4c25808pr0r9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36123",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36123"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Bayesian Active Learning of Ion Loss-Cone Boundaries in Tokamaks",
  "title_text": "Bayesian Active Learning of Ion Loss-Cone Boundaries in Tokamaks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/e4c25808pr0r9",
  "venue": "arXiv",
  "versions": []
}