Point processes and the infinite symmetric group. Part VI: Summary of results

TL;DR

This paper summarizes results connecting harmonic analysis on the infinite symmetric group with probability measures, stochastic point processes, and random matrix theory, revealing new kernels and degenerations with implications for representation theory.

Contribution

It introduces a new connection between random matrix theory and the representation theory of the infinite symmetric group through correlation functions and kernels.

Findings

Correlation functions expressed via hypergeometric series.

Determinantal form of correlation functions with Whittaker kernel.

Identification of new Bessel-type kernels.

Abstract

We give a summary of the results from Parts I-V (math.RT/9804086, math.RT/9804087, math.RT/9804088, math.RT/9810013, math.RT/9810014). Our work originated from harmonic analysis on the infinite symmetric group. The problem of spectral decomposition for certain representations of this group leads to a family of probability measures on an infinite-dimensional simplex, which is a kind of dual object for the infinite symmetric group. To understand the nature of these measures we interpret them as stochastic point processes on the punctured real line and compute their correlation functions. The correlation functions are given by multidimensional integrals which can be expressed in terms of a multivariate hypergeometric series (the Lauricella function of type B). It turns out that after a slight modification (`lifting') of the processes the correlation functions take a common in Random Matrix Theory (RMT) determinantal form with a certain kernel. The kernel is expressed through the classical Whittaker functions. It depends on two parameters and admits a variety of degenerations. They include the well-known in RMT sine and Bessel kernels as well as some other Bessel-type kernels which, to our best knowledge, are new. The explicit knowledge of the correlation functions enables us to derive a number of conclusions about the initial probability measures. We also study the structure of our kernel; this finally leads to a constructive description of the initial measures. We believe that this work provides a new promising connection between RMT and Representation Theory.