Applications of flow models to the generation of correlated lattice QCD ensembles
This provides a practical variance reduction method for lattice QCD researchers, though it appears incremental as it applies existing flow models to a specific domain problem.
The authors tackled the problem of reducing statistical uncertainties in lattice quantum field theory calculations by using machine-learned normalizing flows to generate correlated ensembles of lattice gauge fields. They demonstrated significant variance reduction in three proof-of-concept applications compared to uncorrelated ensembles or direct reweighting.
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables. Three different proof-of-concept applications are demonstrated using a novel residual flow architecture: continuum limits of gauge theories, the mass dependence of QCD observables, and hadronic matrix elements based on the Feynman-Hellmann approach. In all three cases, it is shown that statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting.