On statistical models on super trees

TL;DR

This paper explores the spectral properties of super trees with variable vertex degrees, linking statistical models, spectral theory, and combinatorics, and reveals connections to random matrix ensembles and KPZ scaling.

Contribution

It introduces a novel analysis of spectral properties of super trees and their relation to statistical models, ODE/IM correspondence, and combinatorial path counting.

Findings

Spectral properties of averaged random matrix ensembles are described using Hermite polynomials.

Path counting on super trees relates to area-weighted Dyck paths at small branching velocities.

Spectral statistics of random walks on super trees connect to KPZ scaling phenomena.

Abstract

We consider a particular example of interplay between statistical models related to CFT on one hand, and to the spectral properties of ODE, known as ODE/IS correspondence, on the other hand. We focus at the representation of wave functions of Schr\"odinger operators in terms of spectral properties of associated transfer matrices on "super trees" (the trees whose vertex degree changes with the distance from the root point). Such trees with varying branchings encode the structure of the Fock space of the model. We discuss basic spectral properties of "averaged random matrix ensembles" in terms of Hermite polynomials for the transfer matrix of super trees. At small "branching velocities" we have related the problem of paths counting on super trees to the statistics of area-weighted one-dimensional Dyck paths. We also discuss the connection of the spectral statistics of random walks on super trees with the Kardar-Parisi-Zhang scaling.