Journal Article PUBDB-2025-02451

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Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions

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2025
APS College Park, Md.

Physical review letters 135(2), 021904 () [10.1103/csc2-bj91]
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Report No.: DESY-25-022; JLAB-THY-25-4221; arXiv:2502.04166

Abstract: We present the first extraction of transverse-momentum-dependent distributions of unpolarized quarks from experimental Drell-Yan data using neural networks to parametrize their nonperturbative part. We show that neural networks outperform traditional parametrizations providing a more accurate description of data. This Letter establishes the feasibility of using neural networks to explore the multidimensional partonic structure of hadrons and paves the way for more accurate determinations based on machine-learning techniques.

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Contributing Institute(s):
  1. Theorie-Gruppe (T)
Research Program(s):
  1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
  2. DFG project G:(GEPRIS)409651613 - FOR 2926: Next Generation Perturbative QCD for Hadron Structure: Preparing for the Electron-Ion Collider (409651613) (409651613)
  3. DFG project G:(GEPRIS)430915355 - Multi-Parton Wechselwirkungen und Beiträge mit höherem Twist (430915355) (430915355)
Experiment(s):
  1. No specific instrument

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A Neural-Network Extraction ofUnpolarised Transverse-Momentum-Dependent Distributions
[10.3204/PUBDB-2025-00641]  GO OpenAccess  Download fulltext Files  Download fulltextFulltext Download fulltextFulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS


 Record created 2025-07-18, last modified 2025-08-20


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