Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

• URL: http://arxiv.org/abs/2307.01107v2
• Date: Mon, 12 Feb 2024 10:03:28 GMT
• Title: Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
• Authors: Michele Caselle, Elia Cellini and Alessandro Nada
• Abstract summary: EST represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory. We show that by using a new class of deep generative models it is possible to obtain reliable numerical estimates of EST predictions.
• Score: 49.1574468325115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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