Recognition: unknown
Locality and exponential error reduction in numerical lattice gauge theory
read the original abstract
In non-abelian gauge theories without matter fields, expectation values of large Wilson loops and loop correlation functions are difficult to compute through numerical simulation, because the signal-to-noise ratio is very rapidly decaying for increasing loop sizes. Using a multilevel scheme that exploits the locality of the theory, we show that the statistical errors in such calculations can be exponentially reduced. We explicitly demonstrate this in the SU(3) theory, for the case of the Polyakov loop correlation function, where the efficiency of the simulation is improved by many orders of magnitude when the area bounded by the loops exceeds 1 fm^2.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
Neural network interpolators for Wilson loops
Neural networks parametrize gauge-equivariant trial states for Wilson loops and automatically yield interpolators for ground and excited states in quenched lattice QCD.
-
Wilson loops with neural networks
Neural networks parametrize gauge-invariant interpolators that extract ground-state Wilson loops with improved signal-to-noise ratio compared to traditional methods while preserving gauge invariance.
-
Shear and bulk viscosities of the gluon plasma across the transition temperature from lattice QCD
Lattice QCD results show the shear viscosity to entropy density ratio of gluon plasma reaches a minimum near the transition temperature Tc while the bulk viscosity to entropy density ratio decreases monotonically from...
-
Variance reduction strategies for lattice QCD
Variance reduction schemes based on decompositions of quark propagators have proven useful for precision lattice QCD observables and may help reduce the computational cost of reaching large volumes.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.