Sampling the lattice NambuGoto string using Continuous Normalizing Flows
Abstract
Effective String Theory (EST) represents a powerful nonperturbative approach to describe confinement in YangMills theory that models the confining flux tube as a thin vibrating string. EST calculations are usually performed using the zetafunction regularization: however there are situations (for instance the study of the shape of the flux tube or of the higher order corrections beyond the NambuGoto 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 NambuGoto 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.
 Publication:

Journal of High Energy Physics
 Pub Date:
 February 2024
 DOI:
 10.1007/JHEP02(2024)048
 arXiv:
 arXiv:2307.01107
 Bibcode:
 2024JHEP...02..048C
 Keywords:

 Algorithms and Theoretical Developments;
 Confinement;
 Vacuum Structure and Confinement;
 High Energy Physics  Lattice;
 Computer Science  Machine Learning;
 High Energy Physics  Theory
 EPrint:
 1+28 pages, 11 figures