Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions
We 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.
Authors
- Marco Radici (ORCID: https://orcid.org/0000-0002-4542-9797)
- Chiara Bissolotti (ORCID: https://orcid.org/0000-0003-3061-0144)
- Simone Rodini (ORCID: https://orcid.org/0000-0002-8057-5597)
- Valerio Bertone (ORCID: https://orcid.org/0000-0003-0148-0272)
- L. Rossi (ORCID: https://orcid.org/0000-0001-8118-4160)
- M. Cerutti (ORCID: https://orcid.org/0000-0001-7238-5657)
- Karl J. Friston
Institutions
- Argonne National Laboratory (US)
- Christopher Newport University (US)
- University of Milan (IT)
- University of Pavia (IT)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- Deutsches Elektronen-Synchrotron DESY (DE)
- Thomas Jefferson National Accelerator Facility (US)
- Istituto Nazionale di Fisica Nucleare, Sezione di Pavia (IT)
- CEA Paris-Saclay (FR)
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano (IT)
- Institut de Recherche sur les Lois Fondamentales de l'Univers (FR)
Publication Details
- Journal
- Physical Review Letters
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1103/csc2-bj91
- Citations
- 16
- Primary Topic
- Particle physics theoretical and experimental studies
- Type
- article
- Field-Weighted Citation Impact
- 29.92
Funders
- U.S. Department of Energy
- Deutsche Forschungsgemeinschaft
- Agence Nationale de la Recherche
- Ministero dell'Università e della Ricerca
- Nuclear Physics
- Laboratory Directed Research and Development