Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
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Gradient-flow scales are set for SU(3), SU(5), SU(8) and large-N Yang-Mills down to 0.025 fm using twisted volume reduction and topology-taming algorithms.
Introduces bounds-based stopping criteria and automatic windowing for autocorrelation integrals to estimate statistical errors in lattice field theory Monte Carlo simulations.
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Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
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Scale setting of SU($N$) Yang--Mills theory, topology and large-$N$ volume independence
Gradient-flow scales are set for SU(3), SU(5), SU(8) and large-N Yang-Mills down to 0.025 fm using twisted volume reduction and topology-taming algorithms.
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Bounding statistical errors in lattice field theory simulations
Introduces bounds-based stopping criteria and automatic windowing for autocorrelation integrals to estimate statistical errors in lattice field theory Monte Carlo simulations.