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A Neural-Network Extraction of Unpolarised Transverse-Momentum-Dependent Distributions
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A Neural-Network Extraction of Unpolarised Transverse-Momentum-Dependent Distributions
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We present the first extraction of transverse-momentum-dependent distributions of unpolarised quarks from experimental Drell-Yan data using neural networks to parametrise their nonperturbative part. We show that neural networks outperform traditional parametrisations providing a more accurate description of data. This work establishes the feasibility of using neural networks to explore the multi-dimensional partonic structure of hadrons and paves the way for more accurate determinations based on machine-learning techniques.
Forward citations
Cited by 11 Pith papers
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