Stochastic normalizing flows for Effective String Theory
This work addresses the challenge of efficiently simulating confining flux tubes in lattice field theory, though it appears incremental as it builds on existing flow-based methods.
The authors tackled the problem of studying confinement in pure gauge theories by applying Stochastic Normalizing Flows (SNFs) to Effective String Theory (EST) on the lattice, reporting numerical results for the shape of the flux tube.
Effective String Theory (EST) is a powerful tool used to study confinement in pure gauge theories by modeling the confining flux tube connecting a static quark-anti-quark pair as a thin vibrating string. Recently, flow-based samplers have been applied as an efficient numerical method to study EST regularized on the lattice, opening the route to study observables previously inaccessible to standard analytical methods. Flow-based samplers are a class of algorithms based on Normalizing Flows (NFs), deep generative models recently proposed as a promising alternative to traditional Markov Chain Monte Carlo methods in lattice field theory calculations. By combining NF layers with out-of-equilibrium stochastic updates, we obtain Stochastic Normalizing Flows (SNFs), a scalable class of machine learning algorithms that can be explained in terms of stochastic thermodynamics. In this contribution, we outline EST and SNFs, and report some numerical results for the shape of the flux tube.