On the correlation functions of the domain wall six vertex model

TL;DR

This paper introduces a combinatorial approach to compute correlation functions in the domain wall six vertex model, reproducing known results and extending to multi-point functions using determinant expressions.

Contribution

It provides a new combinatorial method to derive boundary correlation functions and generalizes to multi-point functions in the six vertex model.

Findings

Reproduces boundary 1-point function determinant expression.

Derives boundary 2-point functions using the new approach.

Suggests a framework for expressing general correlation functions as sums over determinants.

Abstract

We propose an (essentially combinatorial) approach to the correlation functions of the domain wall six vertex model. We reproduce the boundary 1-point function determinant expression of Bogoliubov, Pronko and Zvonarev, then use that as a building block to obtain analogous expressions for boundary 2-point functions. The latter can be used, at least in principle, to express more general boundary (and bulk) correlation functions as sums over (products of) determinants.