Algorithmic overlaps in the Baxter-Wu model: cluster dynamics under Novotny-Evertz updates

Abstract

We study the spatial overlap of successive spin configurations generated by Markov chain Monte Carlo simulations of the Baxter-Wu model. Using the Novotny-Evertz sublattice-freezing single-cluster update, we track the mean and variance of the algorithmic overlap across the critical region. We show that, even in this three-spin model, the overlap acts as an algorithmic observable that follows the thermodynamics of the transition: the single-cluster overlap mean behaves like an order parameter, dropping from a finite ordered-phase plateau toward zero across $T_c$. The overlap does not diverge at criticality, instead it remains finite and its finite-size value decays as a clean power law, $U_2(T_c)\sim L^{-ψ}$, over eleven sizes with an exponent $ψ{=}0.378(4)$ smaller than the value $\approx0.42$ found for the Ising and Potts models under standard Fortuin-Kasteleyn cluster dynamics, indicating that it reflects the Novotny-Evertz sublattice-freezing dynamics rather than any static property of the model.