Annealed limit for a diffusive disordered mean-field model with random jumps

TL;DR

This paper analyzes the limit behavior of a large disordered mean-field particle system with jumps, showing convergence to a stochastic differential equation influenced by a Gaussian environment, with explicit convergence rates.

Contribution

It introduces a novel limit theorem for disordered mean-field models with jumps, incorporating a Gaussian environment derived from a CLT, and provides convergence speed estimates.

Findings

Convergence of the particle system to an SDE with Gaussian environment.

Explicit convergence rates for finite-dimensional distributions.

Extension of classical CLT techniques to disordered jump processes.

Abstract

We study a sequence of $N-$particle mean-field systems, each driven by $N$ simple point processes $Z^{N,i}$ in a random environment. Each $Z^{N,i}$ has the same intensity $(f(X^N_{t-}))_t$ and at every jump time of $Z^{N,i},$ the process $X^N$ does a jump of height $U_i/\sqrt{N}$ where the $U_i$ are disordered centered random variables attached to each particle. We prove the convergence in distribution of $X^N$ to some limit process $\bar X$ that is solution to an SDE with a random environment given by a Gaussian variable, with a convergence speed for the finite-dimensional distributions. This Gaussian variable is created by a CLT as the limit of the patial sums of the $U_i.$ To prove this result, we use a coupling for the classical CLT relying on the result of [Koml\'os, Major and Tusn\'ady (1976)], that allows to compare the conditional distributions of $X^N$ and $\bar X$ given the random environment, with the same Markovian technics as the ones used in [Erny, L\"ocherbach and Loukianova (2022)].