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001     623193
005     20250824021413.0
024 7 _ |a Bacchetta:2025ara
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024 7 _ |a arXiv:2502.04166
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024 7 _ |a 10.3204/PUBDB-2025-00641
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037 _ _ |a PUBDB-2025-00641
041 _ _ |a English
088 _ _ |a DESY-25-022
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088 _ _ |a arXiv:2502.04166
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100 1 _ |a Bacchetta, Alessandro
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245 _ _ |a A Neural-Network Extraction ofUnpolarised Transverse-Momentum-Dependent Distributions
260 _ _ |c 2025
336 7 _ |a Preprint
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336 7 _ |a Electronic Article
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336 7 _ |a ARTICLE
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520 _ _ |a 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.
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536 _ _ |a DFG project G:(GEPRIS)409651613 - FOR 2926: Next Generation Perturbative QCD for Hadron Structure: Preparing for the Electron-Ion Collider (409651613)
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536 _ _ |a DFG project G:(GEPRIS)430915355 - Multi-Parton Wechselwirkungen und Beiträge mit höherem Twist (430915355)
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700 1 _ |a Bertone, Valerio
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700 1 _ |a Bissolotti, Chiara
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700 1 _ |a Cerutti, Matteo
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700 1 _ |a Radici, Marco
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700 1 _ |a Rodini, Simone
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700 1 _ |a Rossi, Lorenzo
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700 1 _ |a MAP Collaboration
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856 4 _ |u https://arxiv.org/abs/2502.04166
856 4 _ |u https://bib-pubdb1.desy.de/record/623193/files/HTML-Approval_of_scientific_publication.html
856 4 _ |u https://bib-pubdb1.desy.de/record/623193/files/PDF-Approval_of_scientific_publication.pdf
856 4 _ |u https://bib-pubdb1.desy.de/record/623193/files/2502.04166v1.pdf
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910 1 _ |a Deutsches Elektronen-Synchrotron
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913 1 _ |a DE-HGF
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914 1 _ |y 2025
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