Deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distributions in $b_T$ space from Drell-Yan data

[2608.27907] Deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distributions in $b_T$ space from Drell-Yan data Skip to main content Search Submit Donate Log in High Energy Physics - Phenomenology arXiv:2608.27907 (hep-ph) [Submitted on 28 Aug 2026] Title: Deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distributions in $b_T$ space from Drell-Yan data Authors: I. P. Fernando , D. Keller View PDF HTML (experimental) 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. Subjects: High Energy Physics - Phenomenology (hep-ph) Cite as: arXiv:2608.27907 [hep-ph] (or arXiv:2608.27907v1 [hep-ph] for this version) https://doi.org/10.48550/arXiv.2608.27907 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ishara Fernando [ view email ] [v1] Fri, 28 Aug 2026 04:17:36 UTC (9,776 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: hep-ph < prev | next > new | recent | 2026-08 References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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