Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Author:

Michele Caselle, Elia Cellini, Alessandro Nada

Keyword:

High Energy Physics - Lattice, High Energy Physics - Lattice (hep-lat), Machine Learning (cs.LG), High Energy Physics - Theory (hep-th)

journal:

date:

2023-07-02 16:00:00

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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