First non-asymptotic-scaling determination of the large-N Yang-Mills Λ-parameter yields √(8t₀)Λ_MS(N=∞) = 0.639(36).
Memory efficient finite volume schemes with twisted boundary conditions
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abstract
In this paper we explore a finite volume renormalization scheme that combines three main ingredients: a coupling based on the gradient flow, the use of twisted boundary conditions and a particular asymmetric geometry, that for $SU(N)$ gauge theories consists on a hypercubic box of size $l^2 \times (Nl)^2$, a choice motivated by the study of volume independence in large $N$ gauge theories. We argue that this scheme has several advantages that make it particularly suited for precision determinations of the strong coupling, among them translational invariance, an analytic expansion in the coupling and a reduced memory footprint with respect to standard simulations on symmetric lattices, allowing for a more efficient use of current GPU clusters. We test this scheme numerically with a determination of the $\Lambda$ parameter in the $SU(3)$ pure gauge theory. We show that the use of an asymmetric geometry has no significant impact in the size of scaling violations, obtaining a value $\Lambda_{\overline{MS}} \sqrt{8 t_0} =0.603(17)$ in good agreement with the existing literature. The role of topology freezing, that is relevant for the determination of the coupling in this particular scheme and for large $N$ applications, is discussed in detail.
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hep-lat 2representative citing papers
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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The large-$N$ Yang--Mills $\Lambda$-parameter from step scaling
First non-asymptotic-scaling determination of the large-N Yang-Mills Λ-parameter yields √(8t₀)Λ_MS(N=∞) = 0.639(36).
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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.