Massive Scaling Limit of the Ising Model: Subcritical Analysis and Isomonodromy
S. C. Park

TL;DR
This paper investigates the massive scaling limit of the planar Ising model's spin correlations in a general domain, extending critical case methods to a massive setting and connecting to boundary value problems and Painlevé equations.
Contribution
It introduces a boundary value problem approach for the massive scaling limit of the Ising model in arbitrary domains, generalizing discrete complex analysis techniques from the critical case.
Findings
Spin correlations converge to a boundary value problem in the massive limit.
Reproduces known Painlevé III formula in the full-plane case.
Extends discrete complex analysis to massive perturbations.
Abstract
We study the spin n-point functions of the planar Ising model on a simply connected domain \Omega discretised by the square lattice \delta\mathbb{Z}^{2} under near-critical scaling limit. While the scaling limit on the full-plane \mathbb{C} has been analysed in terms of a fermionic field theory, the limit in general \Omega has not been studied. We will show that, in a massive scaling limit wherein the inverse temperature is scaled \beta\sim\beta_{c}-m_{0}\delta for a constant m_{0}<0, the renormalised spin correlations converge to a continuous quantity determined by a boundary value problem set in \Omega. In the case of \Omega=\mathbb{C} and n=2, this result reproduces the celebrated formula of [WMTB76] involving the Painlev\'e III transcendent. To this end, we generalise the comprehensive discrete complex analytic framework used in the critical setting to the massive setting, which…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTheoretical and Computational Physics · Stochastic processes and statistical mechanics · Opinion Dynamics and Social Influence
