Configurational Mean-Field Reduced Transfer Matrix Method for Ising Systems

TL;DR

This paper introduces a configurational mean-field reduced transfer matrix method for Ising systems, combining Kadanoff's approach with a previous model, providing a new physical perspective on phase transition analysis.

Contribution

The work presents a novel mean-field approach that integrates transfer matrix techniques without presuming phase transitions, offering a more physically accurate framework.

Findings

Analytical critical coupling values derived for different approaches.

Numerical and analytical estimations show deviations from exact results.

The method offers a more physically realistic picture than traditional self-consistent mean-field models.

Abstract

A mean-field method for the hypercubic nearest-neighbor Ising system is introduced and applications to the method are demonstrated. The main idea of this work is to combine the Kadanoff's mean-field approach with the model presented by one of us previously. The mean-field approximation is introduced with the replacement of the central spin in Ising Hamiltonian with an average value of particular spin configuration, i.e, the approximation is taken into account within each configuration. This approximation is used in two different mean-field-type approaches. The first consideration is a pure-mean-field-type treatment in which all the neighboring spins are replaced with the assumed configurational average. The second consideration is introduced by the reduced transfer matrix method. The estimations of critical coupling values of the systems are evaluated both numerically and also analytically by the using of saddle point approximation. The analytical estimation of critical values in the first and second considerations are $ K_{c}=\frac{1}{z} $ and $ (z-2) K_{c}e^{2K_{c}} =1 $ respectively. Obviously, both of the considerations have some significant deviation from the exact treatment. In this work, we conclude that the method introduced here is more appropriate physical picture than self-consistent mean-field-type models, because the method introduced here does not presume the presence of the phase transition from the outset. Consequently, the introduced approach potentially makes our research very valuable mean-field-type picture for phase transition treatment.