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Journal Article | PUBDB-2025-02451 |
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2025
APS
College Park, Md.
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Please use a persistent id in citations: doi:10.1103/csc2-bj91 doi:10.3204/PUBDB-2025-02451
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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A Neural-Network Extraction ofUnpolarised Transverse-Momentum-Dependent Distributions
[10.3204/PUBDB-2025-00641]
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