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Bridges in the random-cluster model

机译:随机集群模型中的桥梁

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摘要

The random-cluster model, a correlated bond percolation model, unifies a range of important models of statistical mechanics in one description, including independent bond percolation, the Potts model and uniform spanning trees. By introducing a classification of edges based on their relevance to the connectivity we study the stability of clusters in this model. We prove several exact relations for general graphs that allow us to derive unambiguously the finite-size scaling behavior of the density of bridges and non-bridges. For percolation, we are also able to characterize the point for which clusters become maximally fragile and show that it is connected to the concept of the bridge load. Combining our exact treatment with further results from conformal field theory, we uncover a surprising behavior of the (normalized) variance of the number of (non-)bridges, showing that it diverges in two dimensions below the value 4 cos 2 ? ( π / 3 ) = 0.2315891 ? of the cluster coupling q . Finally, we show that a partial or complete pruning of bridges from clusters enables estimates of the backbone fractal dimension that are much less encumbered by finite-size corrections than more conventional approaches.
机译:在一个描述中,随机集群模型(相关的关联渗透模型)统一了一系列重要的统计机制模型,包括独立的关联渗透,Potts模型和统一的生成树。通过基于边缘与连接的相关性引入边缘分类,我们研究了该模型中群集的稳定性。我们证明了一些通用图的精确关系,这些关系使我们能够明确地得出桥梁和非桥梁密度的有限尺寸缩放行为。对于渗流,我们还能够描述聚类变得最大易碎的点,并表明它与桥梁荷载的概念有关。将我们的精确处理方法与保形场理论的进一步结果相结合,我们发现了(非)桥数的(归一化)方差令人惊讶的行为,表明它在二维值4 cos 2?以下的二维方向上发散。 (π/ 3)= 0.2315891?群集耦合的q。最后,我们表明,从集群中对桥梁进行部分或全部修剪,可以估算出骨架分形维数,而与传统方法相比,有限维数校正所带来的影响要小得多。

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