Using operator covariance to disentangle scaling dimensions in lattice models
Anders W. Sandvik

TL;DR
This paper introduces a covariance-based method to extract and disentangle multiple scaling dimensions from lattice models' correlation functions, improving analysis of critical phenomena in Monte Carlo simulations.
Contribution
A novel covariance matrix approach that isolates individual scaling dimensions and their descendants in lattice models, demonstrated on Ising and Blume-Capel models.
Findings
Successfully extracted multiple scaling dimensions in 2D and 3D Ising models.
Isolated primary operators and descendants with high precision.
Observed stability of tricritical descendant scaling far from the tricritical point.
Abstract
In critical lattice models, distance () dependent correlation functions contain power laws governed by scaling dimensions of an underlying continuum field theory. In Monte Carlo simulations, the leading dimensions can be extracted by data fitting, which is difficult when two or more powers contribute significantly. Here a method utilizing covariance between multiple lattice operators is developed where the dependent eigenvalues of the covariance matrix reflect scaling dimensions of individual field operators. This disentangling is demonstrated explicitly for conformal field theories. The scheme is first tested on the critical point of the 2D Ising model, where the two primary scaling dimensions and their respective two lowest descendant dimensions are extracted. The 3D Ising model is studied next, revealing the two relevant primaries and their lowest…
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Taxonomy
TopicsTheoretical and Computational Physics
