Studying Effective String Theory using deep generative models
Abstract
Effective String Theory (EST) offers a robust non-perturbative framework for describing confinement in Yang-Mills theory by treating the confining flux tube between a static quark-antiquark pair as a thin, vibrating string. While EST calculations are typically carried out using zeta-function regularization, certain problems-such as determining the flux tube width-are too complex to solve analytically. However, recent studies have demonstrated that EST can be explored numerically by employing deep learning techniques based on generative algorithms. In this work, we provide a brief introduction to EST and this novel numerical approach. Finally, we present results for the width of the Nambu-Got\"o EST.
Keywords
Cite
@article{arxiv.2508.20610,
title = {Studying Effective String Theory using deep generative models},
author = {Michele Caselle and Elia Cellini and Alessandro Nada},
journal= {arXiv preprint arXiv:2508.20610},
year = {2025}
}
Comments
10 pages, 3 figures, 2 tables, contribution to "The XVIth Quark Confinement and the Hadron Spectrum Conference (QCHSC24)", PoS(QCHSC24)034