Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flows
This work addresses the challenge of numerical simulations in lattice gauge theories for physicists, offering an incremental improvement by applying a state-of-the-art deep learning method to existing models.
The paper tackled the problem of efficiently simulating Effective String Theories on the lattice, which are difficult to sample with standard Monte Carlo methods, by using Stochastic Normalizing Flows to study string width and flux density, achieving reliable results that enable a quantitative description of confinement mechanisms.
Flow-based architectures have recently proved to be an efficient tool for numerical simulations of Effective String Theories regularized on the lattice that otherwise cannot be efficiently sampled by standard Monte Carlo methods. In this work we use Stochastic Normalizing Flows, a state-of-the-art deep learning architecture based on non-equilibrium Monte Carlo simulations, to study different effective string models. After testing the reliability of this approach through a comparison with exact results for the Nambu-Gotō model, we discuss results on observables that are challenging to study analytically, such as the width of the string and the shape of the flux density. Furthermore, we perform a novel numerical study of Effective String Theories with terms beyond the Nambu-Gotō action, including a broader discussion on their significance for lattice gauge theories. The combination of these findings enables a quantitative description of the fine details of the confinement mechanism in different lattice gauge theories. The results presented in this work establish the reliability and feasibility of flow-based samplers for Effective String Theories and pave the way for future applications on more complex models.