Statistical fluctuations under resetting: rigorous results

TL;DR

This paper rigorously analyzes the fluctuations of additive functionals in stochastic processes with non-Poissonian resetting, revealing complex behaviors and phase transitions beyond classical renewal theory.

Contribution

It extends renewal-reward process theory to non-Poissonian resetting, establishing large deviation principles and exploring rich phenomenology including dynamical phase transitions.

Findings

Established large deviation principles for additive functionals under resetting

Demonstrated phase transitions in stochastic processes with non-Poissonian resetting

Applied results to reset Brownian motion's occupation time and areas

Abstract

In this paper we investigate the normal and the large fluctuations of additive functionals associated with a stochastic process under a general non-Poissonian resetting mechanism. Cumulative functionals of regenerative processes are very close to renewal-reward processes and inherit most of the properties of the latter. Here we review and use the classical law of large numbers and central limit theorem for renewal-reward processes to obtain same theorems for additive functionals of a stochastic process under resetting. Then, we establish large deviation principles for these functionals by illustrating and applying a large deviation theory for renewal-reward processes that has been recently developed by the author. We discuss applications of the general results to the positive occupation time, the area, and the absolute area of the reset Brownian motion. While introducing advanced tools from renewal theory, we demonstrate that a rich phenomenology accounting for dynamical phase transitions emerges when one goes beyond Poissonian resetting.