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@ARTICLE{Bacchetta:623193,
      author       = {Bacchetta, Alessandro and Bertone, Valerio and Bissolotti,
                      Chiara and Cerutti, Matteo and Radici, Marco and Rodini,
                      Simone and Rossi, Lorenzo},
      collaboration = {{MAP Collaboration}},
      title        = {{A} {N}eural-{N}etwork {E}xtraction of{U}npolarised
                      {T}ransverse-{M}omentum-{D}ependent {D}istributions},
      reportid     = {PUBDB-2025-00641, DESY-25-022. JLAB-THY-25-4221.
                      arXiv:2502.04166},
      year         = {2025},
      abstract     = {We 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.},
      cin          = {T},
      cid          = {I:(DE-H253)T-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / DFG
                      project G:(GEPRIS)409651613 - FOR 2926: Next Generation
                      Perturbative QCD for Hadron Structure: Preparing for the
                      Electron-Ion Collider (409651613) / DFG project
                      G:(GEPRIS)430915355 - Multi-Parton Wechselwirkungen und
                      Beiträge mit höherem Twist (430915355)},
      pid          = {G:(DE-HGF)POF4-611 / G:(GEPRIS)409651613 /
                      G:(GEPRIS)430915355},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2502.04166},
      howpublished = {arXiv:2502.04166},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2502.04166;\%\%$},
      doi          = {10.3204/PUBDB-2025-00641},
      url          = {https://bib-pubdb1.desy.de/record/623193},
}