First non-perturbative lattice determination of the Yang-Mills topological susceptibility slope χ' in the large-N limit using a novel algorithm to avoid topological freezing.
Large-$N$ $SU(N)$ Yang-Mills theories with milder topological freezing
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abstract
We simulate $4d$ $SU(N)$ pure-gauge theories at large $N$ using a parallel tempering scheme that combines simulations with open and periodic boundary conditions, implementing the algorithm originally proposed by Martin Hasenbusch for $2d$ $CP^{N-1}$ models. That allows to dramatically suppress the topological freezing suffered from standard local algorithms, reducing the autocorrelation time of $Q^2$ up to two orders of magnitude. Using this algorithm in combination with simulations at non-zero imaginary $\theta$ we are able to refine state-of-the-art results for the large-$N$ behavior of the quartic coefficient of the $\theta$-dependence of the vacuum energy $b_2$, reaching an accuracy comparable with that of the large-$N$ limit of the topological susceptibility.
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First non-asymptotic-scaling determination of the large-N Yang-Mills Λ-parameter yields √(8t₀)Λ_MS(N=∞) = 0.639(36).
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.
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.
Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.
citing papers explorer
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The topological susceptibility slope $\chi^\prime$ in the large-$N$ limit
First non-perturbative lattice determination of the Yang-Mills topological susceptibility slope χ' in the large-N limit using a novel algorithm to avoid topological freezing.
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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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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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Machine learning for four-dimensional SU(3) lattice gauge theories
Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.