Bootstrap analysis of meromorphic observables in large-N QCD yields universal and SVZ-type bounds that constrain chiral Lagrangian parameters and link hadronic data to asymptotic freedom.
Testing (asymptotic) scaling in Yang-Mills theories in the large-$N_c$ limit
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abstract
TEK reduction is a well-established technique that allows single-site simulations of Yang-Mills theory in the large-$N_c$ limit by exploiting volume reduction induced by twisted boundary conditions. We performed simulations for $SU(841)$ for several gauge couplings and applied standard Wilson flow techniques combined with a tree-level improvement methodology to set the lattice scale. The wide range of gauge couplings covered by our simulations allows us to explore a region in the coupling space where our data exhibits asymptotic scaling and perturbation theory could be used to analyze the behaviour of the $\beta$-function. In this talk, I will review the methodology used and go through the main results we obtained, including a determination of the $\Lambda$-parameter of Yang-Mills theory at large-$N_c$ in $\overline{\text{MS}}$-scheme.
representative citing papers
First non-asymptotic-scaling determination of the large-N Yang-Mills Λ-parameter yields √(8t₀)Λ_MS(N=∞) = 0.639(36).
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.
citing papers explorer
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Bootstrapping Pion Form Factors at Large $N$
Bootstrap analysis of meromorphic observables in large-N QCD yields universal and SVZ-type bounds that constrain chiral Lagrangian parameters and link hadronic data to asymptotic freedom.
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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.