Data collapse in the critical region using finite-size scaling with subleading corrections

TL;DR

This paper introduces a method to incorporate subleading corrections into finite-size scaling analysis, enhancing data collapse techniques for studying critical phenomena in various models.

Contribution

It presents a novel approach to include subleading corrections in finite-size scaling, improving the accuracy of data collapse in critical systems.

Findings

Enhanced data collapse in Ising models and quantum antiferromagnets

Improved analysis of critical points using the new method

Demonstrated effectiveness across classical and quantum models

Abstract

We propose a treatment of the subleading corrections to finite-size scaling that preserves the notion of data collapse. This approach is used to extend and improve the usual Binder cumulant analysis. As a demonstration, we present results for the two- and three-dimensional classical Ising models and the two-dimensional, double-layer quantum antiferromagnet.