{
  "abstract": "We present a retrospective case study of bias-corrected machine learning (ML) estimates of traces of the inverse Dirac operator, $\\text{Tr}\\,M^{-n}$ ($n=1,2,3,4$), using a fixed lattice QCD dataset and examining how the results depend on the relative proportions of the labeled and training sets. Two supervised learning approaches are examined: one using $\\text{Tr}\\,M^{-1}$ as the input feature, and the other employing gauge observables such as the plaquette and rectangle. Beyond the direct estimation of $\\text{Tr}\\,M^{-n}$, we further investigate two derived applications of the ML estimations: the evaluation of the cumulants of the chiral condensate within a single ensemble and that obtained through multi-ensemble reweighting across ensembles with different quark masses. Within this fixed dataset, the bias-corrected estimates show close agreement with the full-data reference under the adopted evaluation criteria, while the uncorrected estimates can exhibit amplified deviations after the nonlinear cumulant and reweighting steps. For the approach using $\\text{Tr}\\,M^{-1}$ as the input feature, nominal solve-count accounting suggests that the Dirac-inversion cost could be reduced to approximately $25.75\\%$ of that of the conventional calculation in the present setup. This value is a cost projection rather than an end-to-end benchmark: it assumes comparable costs for successive inversions and excludes model-training and analysis overhead.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.30416",
  "author_total": 3,
  "authors": [
    {
      "name": "Benjamin J. Choi"
    },
    {
      "name": "Hiroshi Ohno"
    },
    {
      "name": "Akio Tomiya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4z2hhw8pfz1xx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30416",
  "fulltext_url": null,
  "id": "4z2hhw8pfz1xx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30416",
  "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",
      "hep-lat"
    ],
    [
      "openalex",
      "3106|Quantum Chromodynamics and Particle Interactions"
    ]
  ],
  "openalex": "W7204869697",
  "pdf_url": "https://arxiv.org/pdf/2608.30416",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4z2hhw8pfz1xx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "2608.30416",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30416"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-lat",
      "subfield": "high-energy"
    }
  ],
  "title": "Bias-Corrected Machine-Learning Estimation of Chiral Condensate Cumulants: A Retrospective Lattice QCD Case Study",
  "title_text": "Bias-Corrected Machine-Learning Estimation of Chiral Condensate Cumulants: A Retrospective Lattice QCD Case Study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/4z2hhw8pfz1xx",
  "venue": "arXiv",
  "versions": []
}