Interplay of the complete-graph and Gaussian fixed-point asymptotics in finite-size scaling of percolation above the upper critical dimension

For statistical mechanical systems with continuous phase transitions, there are two closely related but subtly different mean-field treatments, the Gaussian fixed point (GFP) in the renormalization group framework 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 d_{c}=6 with periodic and cylindrical boundary conditions. Our results suggest that, with periodic boundaries, the unwrapped correlation length scales as L^{d/6} at the critical point, diverging faster than L above d_{c}. As a consequence, the scaling behaviors of macroscopic quantities with respect to the linear system size L follow the CG asymptotics. The distance-dependent properties, such as the short-distance behavior of the two-point correlation function and the Fourier transformed quantities with nonzero modes, are still controlled by the GFP. With cylindrical boundaries, due to the interplay of the GFP and CG effects, the correlation length along the axial direction of the cylinder scales as ξ_{L}∼L^{(d-1)/5} within the critical window of size O(L^{-2(d-1)/5}), distinct from periodic boundary. A field-theoretical calculation for deriving the scaling of ξ_{L} is also presented. Moreover, the one-point surface correlation function along the axial direction of the cylinder is observed to scale as τ^{(1-d)/2} when the distance τ is short, but then enter a plateau of order L^{-3(d-1)/5} before it decays significantly fast.