Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

• Published in: The journal of high energy physics Vol. 2024; no. 2; pp. 48 - 28
• Main Authors: Caselle, Michele, Cellini, Elia, Nada, Alessandro
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 08.02.2024; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Algorithms and Theoretical Developments; Classical and Quantum Gravitation; Confinement; Elementary Particles; Machine learning; Mathematical models; Physics; Physics and Astronomy; Quantum Field Theories; Quantum Field Theory; Quantum Physics; Regular Article - Theoretical Physics; Regularization; Relativity Theory; Simulation; String Theory; Vacuum Structure and Confinement; Yang-Mills theory; Vacuum Structure and Confinement; Algorithms and Theoretical Developments; Confinement
• ISSN: 1029-8479; 1126-6708; 1127-2236; 1029-8479
• DOI: 10.1007/JHEP02(2024)048

A bstract Effective String Theory (EST) represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory that models the confining flux tube as a thin vibrating string. EST calculations are usually performed using the zeta-function regularization: however there are situations (for instance the study of the shape of the flux tube or of the higher order corrections beyond the Nambu-Goto EST) which involve observables that are too complex to be addressed in this way. In this paper we propose a numerical approach based on recent advances in machine learning methods to circumvent this problem. Using as a laboratory the Nambu-Goto string, we show that by using a new class of deep generative models called Continuous Normalizing Flows it is possible to obtain reliable numerical estimates of EST predictions.