{"title":"Machine learning insights into quark–antiquark interactions: probing field distributions and string tension in QCD","authors":"Wei Kou, Xurong Chen","doi":"10.1140/epjc/s10052-025-13958-9","DOIUrl":null,"url":null,"abstract":"<div><p>Understanding the interactions between quark–antiquark pairs is essential for elucidating quark confinement within the framework of quantum chromodynamics (QCD). This study investigates the field distribution patterns that arise between these pairs by employing advanced machine learning techniques, namely multilayer perceptrons (MLP) and Kolmogorov-Arnold networks (KAN), to analyze data obtained from lattice QCD simulations. The models developed through this training are then applied to calculate the string tension and width associated with chromo flux tubes, and these results are rigorously compared to those derived from lattice QCD. Moreover, we introduce a preliminary analytical expression that characterizes the field distribution as a function of quark separation, utilizing the KAN methodology. Our comprehensive quantitative analysis underscores the potential of integrating machine learning approaches into conventional QCD research.</p></div>","PeriodicalId":788,"journal":{"name":"The European Physical Journal C","volume":"85 3","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1140/epjc/s10052-025-13958-9.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The European Physical Journal C","FirstCategoryId":"4","ListUrlMain":"https://link.springer.com/article/10.1140/epjc/s10052-025-13958-9","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, PARTICLES & FIELDS","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding the interactions between quark–antiquark pairs is essential for elucidating quark confinement within the framework of quantum chromodynamics (QCD). This study investigates the field distribution patterns that arise between these pairs by employing advanced machine learning techniques, namely multilayer perceptrons (MLP) and Kolmogorov-Arnold networks (KAN), to analyze data obtained from lattice QCD simulations. The models developed through this training are then applied to calculate the string tension and width associated with chromo flux tubes, and these results are rigorously compared to those derived from lattice QCD. Moreover, we introduce a preliminary analytical expression that characterizes the field distribution as a function of quark separation, utilizing the KAN methodology. Our comprehensive quantitative analysis underscores the potential of integrating machine learning approaches into conventional QCD research.
期刊介绍:
Experimental Physics I: Accelerator Based High-Energy Physics
Hadron and lepton collider physics
Lepton-nucleon scattering
High-energy nuclear reactions
Standard model precision tests
Search for new physics beyond the standard model
Heavy flavour physics
Neutrino properties
Particle detector developments
Computational methods and analysis tools
Experimental Physics II: Astroparticle Physics
Dark matter searches
High-energy cosmic rays
Double beta decay
Long baseline neutrino experiments
Neutrino astronomy
Axions and other weakly interacting light particles
Gravitational waves and observational cosmology
Particle detector developments
Computational methods and analysis tools
Theoretical Physics I: Phenomenology of the Standard Model and Beyond
Electroweak interactions
Quantum chromo dynamics
Heavy quark physics and quark flavour mixing
Neutrino physics
Phenomenology of astro- and cosmoparticle physics
Meson spectroscopy and non-perturbative QCD
Low-energy effective field theories
Lattice field theory
High temperature QCD and heavy ion physics
Phenomenology of supersymmetric extensions of the SM
Phenomenology of non-supersymmetric extensions of the SM
Model building and alternative models of electroweak symmetry breaking
Flavour physics beyond the SM
Computational algorithms and tools...etc.