Cluster percolation and dynamical scaling in the Baxter--Wu model
Alexandros Vasilopoulos, Michail Akritidis, Nikolaos G. Fytas, Martin Weigel
TL;DR
This study analyzes Fortuin–Kasteleyn–type clusters in the spin-$\tfrac{1}{2}$ Baxter–Wu model by freezing one sublattice to form an effective two-body problem on a honeycomb lattice, enabling FKCK cluster updates. It demonstrates that the resulting stochastic clusters percolate exactly at the thermal critical point $T_c$ and that cluster observables reproduce the Baxter–Wu universality class exponents, with $1/\nu \approx 1.50$, $\beta/\nu = 1/8$, and $\gamma/\nu = 7/4$. The authors also quantify the dynamical scaling of multi- and single-cluster algorithms via integrated autocorrelation times, finding $z\approx 1.16$ and $z\approx 1.25$ respectively, both substantially reducing critical slowing down relative to Metropolis updates. These findings validate the cluster approach for the Baxter–Wu model and suggest pathways for applying similar percolation analyses to related spin systems, including spin-1 variants and gonihedric-like models.
Abstract
We investigate the percolation behavior of Fortuin-Kasteleyn--type clusters in the spin-$1/2$ Baxter--Wu model with three-spin interactions on a triangular lattice. The considered clusters are constructed by randomly freezing one of the three sublattices, resulting in effective pairwise interactions among the remaining spins. Using Monte Carlo simulations combined with a finite-size scaling analysis, we determine the percolation temperature of these stochastic clusters and show that it coincides with the exact thermal critical point of the model. The critical exponents derived from cluster observables are consistent with those of the underlying thermal phase transition. Finally, we analyze the dynamical scaling of the multi-cluster and single-cluster algorithms resulting from the cluster construction, highlighting their efficiency and scaling behavior with system size.
