Self-averaging and criticality: A comparative study in 2d random bond spin models

TL;DR

This study compares the effects of quenched bond randomness on self-averaging in two 2D spin models, revealing significant differences and confirming theoretical predictions about critical behavior and correlation length exponents.

Contribution

It provides a comparative analysis of self-averaging violations in two 2D spin models using the Wang-Landau algorithm, confirming renormalization group predictions.

Findings

Strong violation of self-averaging in the SAF model

Agreement of correlation length exponent with FSS results

Validation of RG predictions for pseudocritical temperature variance

Abstract

We investigate and contrast, via the Wang-Landau (WL) algorithm, the effects of quenched bond randomness on the self-averaging properties of two Ising spin models in 2d. The random bond version of the superantiferromagnetic (SAF) square model with nearest- and next-nearest-neighbor competing interactions and the corresponding version of the simple ferromagnetic Ising model are studied. We find that, the random bond SAF model shows a strong violation of self-averaging, much stronger than that observed in the case of the random bond Ising model. Our analysis of the asymptotic scaling behavior of the variance of the distribution of the sample-dependent pseudocritical temperatures is found to be consistent with the renormalization group prediction of Aharony and Harris. Using this alternative approach, we find estimates of the correlation length exponent $\nu$ in agreement with results obtained from the usual finite-size scaling (FSS) methodology.