Interplay of the complete-graph and Gaussian fixed-point asymptotics in finite-size scaling of percolation above the upper critical dimension
Mingzhong Lu, Sheng Fang, Zongzheng Zhou, Youjin Deng

TL;DR
This study uses large-scale simulations to explore how Gaussian fixed point and complete graph asymptotics influence finite-size scaling in percolation above the upper critical dimension, revealing boundary-dependent behaviors.
Contribution
It provides a detailed analysis of the interplay between GFP and CG effects in finite-size scaling across different boundary conditions above the critical dimension.
Findings
Periodic boundaries: correlation length scales as L^{d/6} at criticality.
Free boundaries: GFP controls finite-size scaling, with some quantities showing CG behavior.
Cylindrical boundaries: correlation length scales as L^{(d-1)/5} along the axis.
Abstract
Percolation has two mean-field theories, the Gaussian fixed point (GFP) and the Landau mean-field theory or the complete graph (CG) asymptotics. By large-scale Monte Carlo simulations, we systematically study the interplay of the GFP and CG effects to the finite-size scaling of percolation above the upper critical dimension with periodic, free, and cylindrical boundary conditions. Our results suggest that, with periodic boundaries, the \emph{unwrapped} correlation length scales as at the critical point, diverging faster than above . As a consequence, the scaling behaviours of macroscopic quantities with respect to the linear system size follow the CG asymptotics. The distance-dependent properties, such as the short-distance behaviour of the two-point correlation function and the Fourier transformed quantities with non-zero modes, are still controlled by…
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Taxonomy
TopicsTheoretical and Computational Physics · Stochastic processes and statistical mechanics · Complex Network Analysis Techniques
