Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD

TL;DR

This paper applies machine learning techniques to analyze quark-antiquark interactions in QCD, focusing on field distributions and string tension, demonstrating the potential of ML to enhance understanding of quark confinement.

Contribution

It introduces novel ML models, specifically multilayer perceptrons and Kolmogorov-Arnold networks, to analyze lattice QCD data and derive analytical expressions for field distributions.

Findings

ML models accurately predict string tension and flux tube width

Proposed analytical expression characterizes field distribution effectively

Machine learning enhances traditional QCD analysis methods

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.