Sampling the Schwinger Model with Gauge-Equivariant Diffusion
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High Energy Physics - Lattice
Title:Sampling the Schwinger Model with Gauge-Equivariant Diffusion
Abstract:We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model. Our work is inspired by recent and growing successes in developing such generative models for ensemble generation in LFT to overcome the well-known critical slowing down problem. We train a U(1)-equivariant score-based generative model to sample gauge link configurations from the marginal Schwinger model. By computing model likelihoods, we obtain unbiased estimates for observables that closely match those produced by MCMC simulations. We also demonstrate improvement over HMC as measured qualitatively by a reduction in topological freezing near critical parameters.
| Comments: | Conference paper at PAI 2026. 6 pages, 1 figure |
| Subjects: | High Energy Physics - Lattice (hep-lat); Strongly Correlated Electrons (cond-mat.str-el); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.27481 [hep-lat] |
| (or arXiv:2606.27481v1 [hep-lat] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27481
arXiv-issued DOI via DataCite (pending registration)
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