Incomplete determinantal processes: from random matrix to Poisson statistics

TL;DR

This paper investigates a class of determinantal processes that interpolate between Poisson and GUE/Ginibre statistics, revealing a universal transition in mesoscopic statistics influenced by particle deletion, characterized by a superposition of Gaussian noise and Poisson process.

Contribution

It introduces a new class of interpolating determinantal processes and characterizes their mesoscopic transition from random matrix to Poisson statistics.

Findings

Universal transition in mesoscopic statistics depending on particle deletion

Explicit characterization of the crossover as a superposition of Gaussian noise and Poisson process

Analysis of cumulants using correlation kernel asymptotics

Abstract

We study linear statistics of a class of determinantal processes which interpolate between Poisson and GUE/Ginibre statistics in dimension 1 or 2. These processes are obtained by performing an independent Bernoulli percolation on the particle configuration of of a log-gas confined in a general potential. We show that, depending on the expected number of deleted particles, there is a universal transition for mesoscopic statistics. Namely, at small scales, the point process still behaves according to random matrix theory, while, at large scales, it needs to be renormalized because the variance of any linear statistic diverges. The crossover is explicitly characterized as the superposition of a H^{1/2}- or H^1- correlated Gaussian noise and an independent Poisson process. The proof consists in computing the limits of the cumulants of linear statistics using the asymptotics of the correlation kernel of the process.