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.

Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Caselle, Michele;Cellini, Elia
;
Nada, Alessandro
2024-01-01

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.
2024
Inglese
Esperti anonimi
2024
2
1
28
28
https://link.springer.com/article/10.1007/JHEP02(2024)048
Effective String Theory, Normalizing flow
no
   "Simons Collaboration on Confinement and QCD Strings" - Cdd 09/06/2022
   QCDSTRING
   SIMONS FOUNDATION
   CASELLE M.
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3
Caselle, Michele; Cellini, Elia; Nada, Alessandro
info:eu-repo/semantics/article
open
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1969175
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