Deep Neural Network extraction of Unpolarized Transverse Momentum Distributions

[2510.17243] Deep Neural Network extraction of Unpolarized Transverse Momentum Distributions Skip to main content Search Submit Donate Log in High Energy Physics - Phenomenology arXiv:2510.17243 (hep-ph) [Submitted on 20 Oct 2025 ( v1 ), last revised 2 May 2026 (this version, v2)] Title: Deep Neural Network extraction of Unpolarized Transverse Momentum Distributions Authors: I. P. Fernando , D. Keller View PDF HTML (experimental) Abstract: Building on the first-ever application of neural networks in TMD phenomenology: "Extraction of the Sivers function with deep neural networks", we now present a momentum space, physics-informed deep learning framework for the direct extraction of unpolarized transverse momentum dependent parton distributions (TMDs) from fixed target Drell-Yan data (E288, E605). Rather than transforming to impact-parameter space, we remain in k and embed a normalized integrand s(x, k; Q) whose auto-convolution produces the observed qT spectra. The extraction proceeds in two steps. Stage I learns the structure kernel S(qT , x1, x2; QM ) by regressing the cross-section with known kinematic prefactors and charge-weighted PDF combinations factored out; experimental and PDF uncertainties are propagated with Monte Carlo replicas. Stage II reconstructs s(x, k; Q) with an end-to-end differentiable k quadrature layer. Applied to Fermilab cross-section data from experiments E288 and E605, the method reproduces the measured qT spectra across Q and yields x and Q dependent TMDs that broaden with Q, with uncertainty bands that consistently propagate experimental, PDF, algorithmic and methodological components. The approach is minimally biased (no factorized Ansatze and no bT transform) and provides a transferable template for polarized TMDs and related QCD inverse problems. Subjects: High Energy Physics - Phenomenology (hep-ph) Cite as: arXiv:2510.17243 [hep-ph] (or arXiv:2510.17243v2 [hep-ph] for this version) https://doi.org/10.48550/arXiv.2510.17243 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1103/p3sg-k524 Focus to learn more DOI(s) linking to related resources Submission history From: Ishara Fernando [ view email ] [v1] Mon, 20 Oct 2025 07:31:01 UTC (6,742 KB) [v2] Sat, 2 May 2026 19:56:29 UTC (6,630 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: hep-ph < prev | next > new | recent | 2025-10 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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