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arXiv:cs.LG· Javad Komijani·· 4 小时前AI 评分28

通过 holonomies 与 corner reweighting 从独立 plaquette 采样 2D 格点上的 SU(N) 规范理论

Sampling SU(N) gauge theory on a 2D lattice from independent plaquettes via holonomies and corner reweighting

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研究者提出一种统计方法,在二维 SU(N) 规范理论中绕开将独立 plaquette 映射到 link 变量的障碍,转而用 holonomy 变量表述作用量,将格点四角的相容性条件转化为条件采样问题:给定群对易子 Z=XYX†Y† 采样 X、Y。

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Abstract:In the Wilson formulation of lattice gauge theory, the fundamental degrees of freedom are group-valued link variables, while the action is a sum over the trace of the plaquettes, the smallest Wilson loops. For generative sampling methods such as normalizing flows, this poses a challenge: the distribution of an individual plaquette is easy to model, but mapping sampled plaquettes to the links is the obstruction. We explore a statistical way around this in two dimensions for the $\mathrm{SU}(N)$ gauge theory with the Wilson plaquette action. The action can be written in terms of holonomy variables, which determine the links once a consistency condition at the four corners of an extended lattice is satisfied. Using a boundary condition that only affects the holonomies, we trade this consistency condition for a conditional sampling problem, which reduces to sampling $X,Y\in\mathrm{SU}(N)$ given the group commutator $Z=XYX^\dagger Y^\dagger$, where $Z\in\mathrm{SU}(N)$ depends on the four corners. Plaquettes are sampled independently, and the resulting configurations carry weights due to the additional conditional sampling. We obtain the density of $Z$, which determines these weights, in closed form for $\mathrm{SU}(2)$ and $\mathrm{SU}(3)$. A normalizing flow models the single-plaquette distribution for $\mathrm{SU}(2)$ and $\mathrm{SU}(3)$; for the group-commutator problem we use a closed-form sampler for $\mathrm{SU}(2)$ and a trained normalizing flow for $\mathrm{SU}(3)$. In our tests on $2\times2$ and $32\times32$ lattices at two couplings each, per-plaquette acceptance rates exceed $99\%$ and the effective sample size of the final weights is moderate, typically above one half.
Comments: 21 pages, 7 figures
Subjects: High Energy Physics - Lattice (hep-lat); Machine Learning (cs.LG); High Energy Physics - Theory (hep-th)
Cite as: arXiv:2610.09147 [hep-lat]
  (or arXiv:2610.09147v1 [hep-lat] for this version)
  https://doi.org/10.48550/arXiv.2610.09147

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Javad Komijani [view email]
[v1] Tue, 6 Oct 2026 21:42:32 UTC (2,133 KB)

来源:arXiv:cs.LG · arxiv.org