Abstract

We impose the uniform probability measure on the set of all discrete Gelfand-Tsetlin patterns of depth $n$ with the particles on row $n$ in deterministic positions. These systems equivalently describe a broad class of random tilings models, and are closely related to the eigenvalue minor processes of a broad class of random Hermitian matrices. They have a determinantal structure, with a known correlation kernel. We rescale the systems by $\frac1n$, and examine the asymptotic behaviour, as $n \to \infty$, under weak asymptotic assumptions for the (rescaled) particles on row $n$: The empirical distribution of these converges weakly to a probability measure with compact support, and they otherwise satisfy mild regulatory restrictions. We prove that the correlation kernel of particles in the neighbourhood of `typical edge points' convergences to the extended Airy kernel. To do this, we first find an appropriate scaling for the fluctuations of the particles. We give an explicit parameterisation of the asymptotic edge, define an analogous non-asymptotic edge curve (or finite $n$-deterministic equivalent), and choose our scaling such that that the particles fluctuate around this with fluctuations of order $O(n^{-\frac13})$ and $O(n^{-\frac23})$ in the tangent and normal directions respectively. While the final results are quite natural, the technicalities involved in studying such a broad class of models under such weak asymptotic assumptions are unavoidable and extensive.

Highlights

  • We use the scaling in Lemma 2.9 to show that the relevant roots and derivatives of the steepest descent functions have well-behaved asymptotic behaviours, and these result in simple Taylor expansions for the steepest descent functions give in Corollary 3.3

  • In papers [8, 9], we explore the global asymptotic shapes of random tilings of a more restricted class of polygons, but we allow more general boundary/asymptotic conditions which results in some important differences

  • Since t is a root of f(χ,η) of multiplicity 2, (1.7) and steepest descent analysis intuitively imply that universal edge asymptotic behaviour should be observed

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Summary

Introduction

In this paper we consider universal edge behaviour of random systems of discrete interlaced particles referred to as Gelfand–Tsetlin patterns. For all n sufficiently large, the fluctuations have order the tangent direction (of the asymptotic edge) and the (1, 0), τ1 and τ2 measure the size of the fluctuations, and σ1 Petrov chooses this scaling to ensure convergence to the form of the extended Airy kernel defined in [24], evaluated at (τ1, σ1) and (τ2, σ2). We use the scaling in Lemma 2.9 to show that the relevant roots and derivatives of the steepest descent functions have well-behaved asymptotic behaviours, and these result in simple Taylor expansions for the steepest descent functions give in Corollary 3.3 We use these in Theorems 1.10 and 1.11 to prove convergence to the form of the extended Airy kernel given in (1.21), evaluated at (u, r) and (v, s). We hope that our techniques would help to study the determinantal minor processes of other ensembles of random matrices

The determinantal structure of discrete Gelfand–Tsetlin patterns
The asymptotic “shape” of discrete Gelfand–Tsetlin patterns
Motivation and statement of main results
Other asymptotic situations and conjectures
Notation and terminology
The roots of the steepest descent functions
The roots of ft
The roots of fn and fn
The rates of convergence
Steepest descent analysis
Local asymptotic behaviour
Alternative contour integral expressions
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