Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
Abstract
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.
- 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
- E-Print:
- 1+28 pages, 11 figures