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Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions
Alessandro Bacchetta1,2, Valerio Bertone3, Chiara Bissolotti4
1Università di Pavia, Dipartimento di Fisica, via Bassi 6, I-27100 Pavia, Italy.
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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