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Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions

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arxiv 2207.08945 v3 pith:KSHV234J submitted 2022-07-18 hep-lat cs.LG

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions

Ryan Abbott , Michael S. Albergo , Denis Boyda , Kyle Cranmer , Daniel C. Hackett , Gurtej Kanwar , S\'ebastien Racani\`ere , Danilo J. Rezende
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Fernando Romero-L\'opez Phiala E. Shanahan Betsy Tian Julian M. Urban
This is my paper
classification hep-lat cs.LG
keywords theoriesfieldlatticesamplingfermionicflowflow-basedgauge-equivariant
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art lattice field theory calculations, making this development critical to the practical application of flow models to theories such as QCD. Methods by which flow-based sampling approaches can be improved via standard techniques such as even/odd preconditioning and the Hasenbusch factorization are also outlined. Numerical demonstrations in two-dimensional U(1) and SU(3) gauge theories with $N_f=2$ flavors of fermions are provided.

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Cited by 6 Pith papers

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    Fully convolutional diffusion models trained on small lattices transfer to unseen larger volumes for 2D/3D phi^4 sampling across phases, matching or beating same-size training on most observables.

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  4. Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory

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  6. Machine learning for four-dimensional SU(3) lattice gauge theories

    hep-lat 2026-04 unverdicted novelty 3.0

    Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results to...