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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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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.
χ_c1(3872) is treated as a bound state in adjoint-meson Born-Oppenheimer potentials within a diabatic model, with calculated spin splittings and decay widths for its multiplet and bottom analogs after tuning to threshold.
citing papers explorer
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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.
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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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$\boldsymbol{\chi_{c1}}(3872)$ and its Partners in the Diabatic Born-Oppenheimer Approximation for QCD
χ_c1(3872) is treated as a bound state in adjoint-meson Born-Oppenheimer potentials within a diabatic model, with calculated spin splittings and decay widths for its multiplet and bottom analogs after tuning to threshold.