Abstract

We consider consistent dynamics for non-intersecting birth and death chains, originating from dualities of stochastic coalescing flows and one dimensional orthogonal polynomials. As corollaries, we obtain unified and simple probabilistic proofs of certain key intertwining relations between multivariate Markov chains on the levels of some branching graphs. Special cases include the dynamics on the Gelfand-Tsetlin graph considered in the seminal work of Borodin and Olshanski in [10] and the ones on the BC-type graph recently studied by Cuenca in [17]. Moreover, we introduce a general inhomogeneous random growth process with a wall that includes as special cases the ones considered by Borodin and Kuan [8] and Cerenzia [15], that are related to the representation theory of classical groups and also the Jacobi growth process more recently studied by Cerenzia and Kuan [16]. Its most important feature is that, this process retains the determinantal structure of the ones studied previously and for the fully packed initial condition we are able to calculate its correlation kernel explicitly in terms of a contour integral involving orthogonal polynomials. At a certain scaling limit, at a finite distance from the wall, one obtains for a single level discrete determinantal ensembles associated to continuous orthogonal polynomials, that were recently introduced by Borodin and Olshanski in [11], and that depend on the inhomogeneities.

Highlights

  • 1.1 Determinantal structures in inhomogeneous random growth modelsThis work revolves around two sets of closely related problems and ideas

  • In addition to being interesting in its own right a further motivation for this study is the following phenomenon: the exact solvability of a wide class of (1+1)-dimensional models such as the Totally Asymmetric Simple Exclusion Process (TASEP) is a by-product of the fact that they appear as projections of these higher dimensional models, see [7]

  • As explained in the previous subsection, the evolved Gibbs measures for these dynamics are given as products of determinants and by making use of the famous Eynard-Mehta Theorem, in particular a generalization to interlacing particles first studied by Borodin and Rains it is standard that there is an underlying determinantal structure for this point process

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Summary

Determinantal structures in inhomogeneous random growth models

The construction of consistent dynamics on the levels of certain branching graphs and the other is, the exact computation of correlations in random stepped surface growth processes These probabilistic models can be viewed as dynamics on (discrete) interlacing arrays, namely multilevel configurations of particles that satisfy some constraints (that we make precise below), see Figure 2 below for an illustration.

Intertwinings and consistent multilevel dynamics
Alternating construction
Explicit computation of correlation kernel and scaling limit
Further results
Outlook and questions
Contents of the paper
General facts on birth and death chains and their duals
Discrete coalescing flow and two-level process
Intertwinings
Push-block dynamics for the two-level process
Multilevel process construction
Consistent dynamics for multilevel processes
General setup of branching graphs
Method of intertwiners and semigroups on the boundary
Examples of branching graphs
Examples of consistent dynamics
Birth and death chain orthogonal polynomials
Branching rules for multivariate Karlin-McGregor polynomials
Coherent measures
Evolution operators for coherent measures and their basic properties
Positivity of evolution operators and coherent measures
10 Correlation kernels
10.2 Large time and finite distance from wall limit
11.1 Technical results
11.2 Projective chains from branching of functions
11.3 Factorization implies extremality

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