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Equivariant flow-based sampling for lattice gauge theory

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arxiv 2003.06413 v1 pith:4VKY4VGJ submitted 2020-03-13 hep-lat cond-mat.stat-mechcs.LG

Gurtej Kanwar , Michael S. Albergo , Denis Boyda , Kyle Cranmer , Daniel C. Hackett , Sébastien Racanière , Danilo Jimenez Rezende , Phiala E. Shanahan This is my paper · ORCID
classification hep-latcond-mat.stat-mechcs.LG
keywords samplinggaugeflow-basedlatticetheoryalgorithmsapplicationapproach
verification ladder T0 review T1 audit T2 compute T3 formal
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We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that near critical points in parameter space the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as Hybrid Monte Carlo and Heat Bath.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  3. Flow-Based Surrogates for High-Dimensional Likelihoods in Experimental Neutrino Physics

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    A hybrid coupling-plus-autoregressive normalizing flow trained on a 110-parameter T2K-like near-detector likelihood reaches 98% relative ESS versus 5% for the post-fit Gaussian and matches MCMC flux predictions.

  4. Parallel Tempered Metadynamics for full QCD

    hep-lat 2026-07 conditional novelty 5.0 of 10

    In N_f=2 staggered QCD at beta=1.15, PT-MetaD tunnels between topological sectors while RHMC remains frozen, yielding chi_top V = 0.127(33).

  5. Diffusion Models for SU(2) Lattice Gauge Theory in Two Dimensions

    hep-lat 2026-02 conditional novelty 5.0 of 10

    A flat-space quaternion diffusion model, trained at β=2.0 on an 8×8 lattice, reproduces the exact SU(2) plaquette to |Δ|≤0.001 near the training coupling and within 0.06 over β∈[1,4].

  6. Symmetry-preserving neural networks in lattice field theories

    hep-lat 2025-06 conditional novelty 4.0 of 10

    Translation- and gauge-equivariant neural networks (L-CNNs) predict Wilson loops, topological charge, and flux observables with orders-of-magnitude lower error than symmetry-breaking baselines, and neural gradient flo...

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