Exploring nucleon structure with the self-organizing maps algorithm

2014/11/10 by Evan Askanazi, Evan M. Askanazi, Katherine A Holcomb +2
Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Computer science #Distribution (mathematics) #High-Energy Particle Collisions Research #Mathematical analysis #Mathematics #Nucleon #Optics #Particle physics #Particle physics theoretical and experimental studies #Parton #Physics #Quantum Chromodynamics and Particle Interactions #Quantum chromodynamics #Scattering #Self-organization #Self-organizing map #Statistical physics #hep-ex #hep-ph #nlin.AO

paper · pdf · doi:10.1088/0954-3899/42/3/034030

15 pages, 13 figures. arXiv admin note: substantial text overlap with arXiv:1309.7085

arxiv created 2014/11/10 · openalex publication_date 2015/02/05 · arxiv updated 2015/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We discuss the application of an alternative type of neural network, the self-organizing map to extract parton distribution functions from various hard scattering processes.

Citations

• Self-Organizing Maps Parametrization of Deep Inelastic Structure Functions with Error Determination
• Nucleon and pion distribution functions in the valence region
• Combined PDF and strong coupling uncertainties at the LHC with NNPDF2.0
• Generalized parton distributions
• Isospin dependence of power corrections in deep inelastic scattering
• A determination of parton distributions with faithful uncertainty estimation