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Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
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DOI：10.1007/JHEP02(2024)048.png)
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摘要
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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.
Keyword:
Algorithms and Theoretical Developments
Confinement
Vacuum Structure and Confinement
期刊
IF:
5.5
论文数:
3.9W
被引数:
13.7W
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