Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910325085831168 |
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| author | Caselle, Michele Cellini, Elia Nada, Alessandro |
| author_facet | Caselle, Michele Cellini, Elia Nada, Alessandro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_01107 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows Caselle, Michele Cellini, Elia Nada, Alessandro High Energy Physics - Lattice Machine Learning High Energy Physics - Theory 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. |
| title | Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows |
| topic | High Energy Physics - Lattice Machine Learning High Energy Physics - Theory |
| url | https://arxiv.org/abs/2307.01107 |