Neural networks parametrize gauge-invariant interpolators that extract ground-state Wilson loops with improved signal-to-noise ratio compared to traditional methods while preserving gauge invariance.
Precision computation of hybrid static potentials in SU(3) lattice gauge theory
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abstract
We perform a precision computation of hybrid static potentials with quantum numbers $\Lambda_\eta^\epsilon = \Sigma_g^-,\Sigma_u^+,\Sigma_u^-,\Pi_g,\Pi_u,\Delta_g,\Delta_u$ using SU(3) lattice gauge theory. The resulting potentials are used to estimate masses of heavy $\bar{c} c$ and $\bar{b} b$ hybrid mesons in the Born-Oppenheimer approximation. Part of the lattice gauge theory computation, which we discuss in detail, is an extensive optimization of hybrid static potential creation operators. The resulting optimized operators are expected to be essential for future projects concerning the computation of 3-point functions as e.g. needed to study spin corrections, decays or the gluon distribution of heavy hybrid mesons.
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hep-lat 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Neural networks parametrize gauge-equivariant trial states for Wilson loops and automatically yield interpolators for ground and excited states in quenched lattice QCD.
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Wilson loops with neural networks
Neural networks parametrize gauge-invariant interpolators that extract ground-state Wilson loops with improved signal-to-noise ratio compared to traditional methods while preserving gauge invariance.
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Neural network interpolators for Wilson loops
Neural networks parametrize gauge-equivariant trial states for Wilson loops and automatically yield interpolators for ground and excited states in quenched lattice QCD.