Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions
Alessandro Bacchetta, Valerio Bertone, Chiara Bissolotti, M. Cerutti, Marco Radici, Simone Rodini, Lorenzo Rossi
Abstract
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.
Topics & Concepts
Artificial neural networkHadronPhysicsParticle physicsQuarkTransverse planeExtraction (chemistry)Momentum (technical analysis)Statistical physicsComputer scienceArtificial intelligenceChromatographyEngineeringChemistryEconomicsStructural engineeringFinanceParticle physics theoretical and experimental studiesQuantum Chromodynamics and Particle InteractionsHigh-Energy Particle Collisions Research