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

Year: 2025

Type: article

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.

Source: DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron)

Authors: Bacchetta, Alessandro, Bertone, Valerio, Bissolotti, Chiara, Cerutti, Matteo, Radici, Marco, Rodini, Simone, Rossi, Lorenzo, MAP Collaboration

Language: English

Topic: Quantum Chromodynamics and Particle Interactions

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7075679315

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