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000623193 1001_ $$0P:(DE-HGF)0$$aBacchetta, Alessandro$$b0
000623193 245__ $$aA Neural-Network Extraction ofUnpolarised Transverse-Momentum-Dependent Distributions
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000623193 520__ $$aWe present the first extraction from experimental Drell-Yan data of unpolarised transverse-momentum-dependent distributions using neural networks to parametrise their nonperturbativepart. We show that neural networks outperform traditional parametrisations achieving a betterdescription of data. This work not only establishes the feasibility of using neural networks to ex-plore the multi-dimensional partonic structure of hadrons, but also paves the way to more accuratedeterminations which exploit machine-learning techniques.
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000623193 7001_ $$aBertone, Valerio$$b1
000623193 7001_ $$0P:(DE-HGF)0$$aBissolotti, Chiara$$b2$$eCorresponding author
000623193 7001_ $$aCerutti, Matteo$$b3
000623193 7001_ $$aRadici, Marco$$b4
000623193 7001_ $$0P:(DE-H253)PIP1098620$$aRodini, Simone$$b5$$udesy
000623193 7001_ $$aRossi, Lorenzo$$b6
000623193 7001_ $$0P:(DE-HGF)0$$aMAP Collaboration$$b7$$eCollaboration author
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