2006/07/11 by Rojo, Joan
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions
paper · pdf · doi:10.48550/arxiv.hep-ph/0607122
openalex publication_date 2006/07/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce the neural network approach to the parametrization of parton distributions. After a general introduction, we present in detail our approach to parametrize experimental data, based on a combination of Monte Carlo methods and neural networks. We apply this strategy first in three different cases: the proton structure function, hadronic tau decays and B meson decay spectra. Finally we describe the neural network approach applied to the parametrization of parton distribution functions, and present results on the nonsinglet parton distribution.