Speaker
Description
Extracting continuum properties from discretized quantum field theories is significantly hindered by lattice artifacts. Fixed-point (FP) actions, defined via renormalization group transformations, offer an elegant solution by suppressing these artifacts even on coarse lattices. In this work, we employ gauge-covariant convolutional neural networks to parameterize an FP action for four-dimensional SU(3) gauge theory. We show that the gradient flow of the FP action is formally free of lattice artifacts at tree level, enabling the extraction of continuum physics with improved accuracy. Furthermore, our enhanced parameterizations facilitate efficient Hybrid Monte Carlo simulations, effectively mitigating challenges such as critical slowing down and topological freezing. Our results underscore the potential of machine learning techniques to advance lattice QCD studies with reduced discretization errors, a critical step toward precision tests of the Standard Model.
| AI keywords | Gauge equivariant neural networks; L-CNNs; renormalization group learning; physics-informed machine learning |
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