Nested Stochastic Resetting: Nonequilibrium Steady-states and Exact Correlations

TL;DR

This paper analytically investigates a chain of diffusive processes with nested stochastic resetting, deriving steady-state distributions and correlations, thus providing insights into nonequilibrium steady states with unilateral interactions.

Contribution

It introduces a novel analytical framework for nested stochastic resetting processes, deriving exact steady-state statistics and correlations in a many-particle nonequilibrium system.

Findings

Derived stationary distributions for each process.

Calculated exact two-point correlations between processes.

Mapped the problem to ordering statistics of random counting processes.

Abstract

Stochastic resetting breaks detailed balance and drives the formation of nonequilibrium steady states . Here, we consider a chain of diffusive processes $x_i(t)$ that interact unilaterally: at random time intervals, the process $x_n$ stochastically resets to the instantaneous value of $x_{n-1}$. We derive analytically the steady-state statistics of these nested stochastic resetting processes including the stationary distribution for each process as well as its moments. We are also able to calculate exactly the steady-state two-point correlations $\langle x_n x_{n+j}\rangle$ between processes by mapping the problem to one of the ordering statistics of random counting processes. Understanding statistics and correlations in many-particle nonequilibrium systems remains a formidable challenge and our results provide an example of such tractable correlations. We expect this framework will both help build a model-independent framework for random processes with unilateral interactions and find immediate applications, e.g. in the modelling of lossy information propagation.