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Towards glueball masses of large-N SU(N) pure-gauge theories without topological freezing

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arxiv 2205.06190 v2 pith:AH3FBDKM submitted 2022-05-12 hep-lat hep-phhep-th

Towards glueball masses of large-N SU(N) pure-gauge theories without topological freezing

classification hep-lat hep-phhep-th
keywords mathittopologicalfreezingglueballchargegaugelatticemasses
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In commonly used Monte Carlo algorithms for lattice gauge theories the integrated autocorrelation time of the topological charge is known to be exponentially-growing as the continuum limit is approached. This $\mathit{topological}\,\,\textit{freezing}$, whose severity increases with the size of the gauge group, can result in potentially large systematics. To provide a direct quantification of the latter, we focus on $\mathrm{SU}(6)$ Yang--Mills theory at a lattice spacing for which conventional methods associated to the decorrelation of the topological charge have an unbearable computational cost. We adopt the recently proposed $\mathit{parallel}\,\,\mathit{tempering}\,\,\mathit{on}\,\,\mathit{boundary}\,\,\mathit{conditions}$ algorithm, which has been shown to remove systematic effects related to topological freezing, and compute glueball masses with a typical accuracy of $2-5\%$. We observe no sizeable systematic effect in the mass of the first lowest-lying glueball states, with respect to calculations performed at nearly-frozen topological sector.

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Cited by 2 Pith papers

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  1. Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory

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    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.

  2. Scale setting of SU($N$) Yang--Mills theory, topology and large-$N$ volume independence

    hep-lat 2025-11 unverdicted novelty 5.0

    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.