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000633986 1001_ $$0P:(DE-HGF)0$$aBacchetta, Alessandro$$b0
000633986 245__ $$aNeural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions
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000633986 520__ $$aWe present the first extraction of transverse-momentum-dependent distributions of unpolarized quarks from experimental Drell-Yan data using neural networks to parametrize their nonperturbative part. We show that neural networks outperform traditional parametrizations providing a more accurate description of data. This Letter establishes the feasibility of using neural networks to explore the multidimensional partonic structure of hadrons and paves the way for more accurate determinations based on machine-learning techniques.
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000633986 7001_ $$0P:(DE-H253)PIP1033182$$aBertone, Valerio$$b1
000633986 7001_ $$0P:(DE-HGF)0$$aBissolotti, Chiara$$b2
000633986 7001_ $$0P:(DE-HGF)0$$aCerutti, Matteo$$b3$$eCorresponding author
000633986 7001_ $$0P:(DE-HGF)0$$aRadici, Marco$$b4
000633986 7001_ $$0P:(DE-H253)PIP1098620$$aRodini, Simone$$b5
000633986 7001_ $$0P:(DE-HGF)0$$aRossi, Lorenzo$$b6
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000633986 773__ $$0PERI:(DE-600)1472655-5$$a10.1103/csc2-bj91$$gVol. 135, no. 2, p. 021904$$n2$$p021904$$tPhysical review letters$$v135$$x0031-9007$$y2025
000633986 7870_ $$0PUBDB-2025-00641$$aBacchetta, Alessandro et.al.$$d2025$$iIsParent$$rDESY-25-022 ; JLAB-THY-25-4221 ; arXiv:2502.04166$$tA Neural-Network Extraction ofUnpolarised Transverse-Momentum-Dependent Distributions
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