Corrections to scaling in geometrical clusters of the 2D Ising model

TL;DR

This paper investigates how different definitions of geometrical clusters in the 2D Ising model affect corrections to scaling, using Monte Carlo simulations and finite-size scaling analysis.

Contribution

It introduces a detailed analysis of corrections to scaling in geometrical clusters based on various cluster definitions in the 2D Ising model.

Findings

Including all percolating clusters reduces corrections to scaling.

Percolation strength is less affected by cluster definition choices.

Different cluster definitions significantly influence correction magnitudes.

Abstract

We study the scaling of the average cluster size and percolation strength of geometrical clusters for the two-dimensional Ising model. By means of Monte Carlo simulations and a finite-size scaling analysis we discuss the appearance of corrections to scaling for different definitions of cluster sets. We find that including all percolating clusters, or excluding only clusters that percolate in one but not the other direction, leads to smaller corrections to scaling for the average cluster size as compared to the other definitions considered. The percolation strength is less sensitive to the definitions used.