Preprint

PUBDB-2025-00641

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A Neural-Network Extraction ofUnpolarised Transverse-Momentum-Dependent Distributions

Bacchetta, A. ; Bertone, V. ; Bissolotti, C. (Corresponding author) ; Cerutti, M. ; Radici, M. ; Rodini, S.DESY* ; Rossi, L. ; MAP Collaboration (Collaboration author)

2025

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Please use a persistent id in citations: doi:10.3204/PUBDB-2025-00641

Report No.: DESY-25-022; JLAB-THY-25-4221; arXiv:2502.04166

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.

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Contributing Institute(s):

1. Theorie-Gruppe (T)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
2. DFG project G:(GEPRIS)409651613 - FOR 2926: Next Generation Perturbative QCD for Hadron Structure: Preparing for the Electron-Ion Collider (409651613) (409651613)
3. DFG project G:(GEPRIS)430915355 - Multi-Parton Wechselwirkungen und Beiträge mit höherem Twist (430915355) (430915355)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2025

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Database coverage:
; ; Published

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Bacchetta, A. ; Bertone, V.Extern* ; Bissolotti, C. ; Cerutti, M. (Corresponding author) ; Radici, M. ; Rodini, S.DESY* ; Rossi, L. ; MAP Collaboration
Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions
Physical review letters 135(2), 021904 (2025) [10.1103/csc2-bj91]2025     Files  Fulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS

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