{
  "abstract": "We present a physics-informed deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distribution functions (TMDPDFs) in impact-parameter space from Drell--Yan data. The perturbative contribution is computed with a resummed $W$ term using $\\mathrm{N}^{3}\\mathrm{LL}$ evolution, strict-NLO hard and operator-product-expansion matching, and smooth profile scales at small and large $b_T$. A compact feature-wise linear modulation network learns only a shared nonperturbative factor $F_{NP}(x,b_T)$; the collinear PDFs, hard factor, evolution kernel, matching coefficients, and Fourier--Bessel transform remain fixed. The primary result is a smooth light-flavor $b_T$-space TMD ensemble and its cross-section-level validation. The reported $k_T$ distributions are regularized finite-$b_T$ Hankel transforms, not independent momentum-space fits. As a separate robustness test, a smooth finite-$Y$ transition is applied to 24 additional Tevatron points extending to $q_T/Q\\simeq0.30$. The nominal 329-point fit is unchanged, and the results remain stable when $F_{\\rm NP}$ is held fixed while the transition profile is varied. An independent 122-bin Tevatron $\\mathrm{N}^{3}\\mathrm{LL}+\\mathrm{NNLO}$ $W+Y$ grid provides a direct perturbative benchmark. A separate $W+Y$ candidate using the specified non-LHCb finite-$Y$ inputs is retained as an identifiability study.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.27907",
  "author_total": 2,
  "authors": [
    {
      "name": "I. P. Fernando"
    },
    {
      "name": "D. Keller"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cczcdtqemp0ds.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.27907",
  "fulltext_url": null,
  "id": "cczcdtqemp0ds",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.27907",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "openalex",
      "3106|Quantum Chromodynamics and Particle Interactions"
    ]
  ],
  "openalex": "W7204745149",
  "pdf_url": "https://arxiv.org/pdf/2608.27907",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cczcdtqemp0ds.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "2608.27907",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.27907"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ph",
      "subfield": "high-energy"
    }
  ],
  "title": "Deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distributions in $b_T$ space from Drell-Yan data",
  "title_text": "Deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distributions in b_T space from Drell-Yan data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/cczcdtqemp0ds",
  "venue": "arXiv",
  "versions": []
}