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Gauge cooling in complex Langevin for QCD with heavy quarks

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arxiv 1211.3709 v2 pith:YR3TBR43 submitted 2012-11-15 hep-lat hep-phhep-th

classification hep-lathep-phhep-th
keywords complexcoolinggaugeheavylangevinmethodquarksresults
verification ladder T0 review T1 audit T2 compute T3 formal

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We employ a new method, "gauge cooling", to stabilize complex Langevin simulations of QCD with heavy quarks. The results are checked against results obtained with reweigthing; we find agreement within the estimated errors. The method allows us to go to previously unaccessible high densities.

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Cited by 5 Pith papers

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

  1. Designing weight regularizations based on Lefschetz thimbles to stabilize complex Langevin

    hep-lat 2024-12 conditional novelty 7.0 of 10

    A regularization inspired by Lefschetz thimbles stabilizes complex Langevin simulations in toy models, with a bias-correction step that restores the original expectation values.

  2. The Role of Integration Cycles in Complex Langevin Simulations

    hep-lat 2024-12 conditional novelty 6.0 of 10

    Complex Langevin results in one- and two-dimensional toy models match a linear combination of integration cycles when boundary terms vanish, and the kernel choice controls which cycles contribute.

  3. Diffusion models learn distributions generated by complex Langevin dynamics

    hep-lat 2024-12 conditional novelty 6.0 of 10

    Diffusion models reproduce the distributions sampled by complex Langevin dynamics in a Gaussian and a quartic toy model with complex mass.

  4. Combining complex Langevin dynamics with score-based and energy-based diffusion models

    hep-lat 2025-10 conditional novelty 5.0 of 10

    Energy-based diffusion models trained on complex Langevin data produce an explicit energy function for the sampled distribution, enabling MCMC without re-simulation.

  5. Diffusion models and stochastic quantisation in lattice field theory

    hep-lat 2024-12 unverdicted novelty 2.0 of 10

    Diffusion models, whose backward denoising step resembles stochastic quantisation, can learn from HMC data to generate configurations for 2D scalar lattice field theory.

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