First passage time and information of a one-dimensional Brownian particle with stochastic resetting to random positions

TL;DR

This paper investigates how stochastic resetting to random positions affects the first passage times and entropy of a one-dimensional Brownian particle, introducing a novel analytical method and exploring different entropy regimes and their interplay with reset dynamics.

Contribution

It presents a new analytical approach to compute first passage metrics for Brownian particles with random reset positions, analyzing entropy regimes and their effects on first passage times.

Findings

Mean first passage time can be minimized in various entropy regimes.

The reset position distribution's second moment influences the MFPT and MFPE.

A mechanism allows entropy per reset to vary independently of overall first passage entropy.

Abstract

We explore the effects of stochastic resetting to random positions of a Brownian particle on first passage times and Shannon's entropy. We explore the different entropy regimes, namely, the \textit{externally-driven}, the \textit{zero-entropy} and the \textit{Maxwell demon} regimes. We show that the mean first passage time (MPFT) minimum can be found in any of these regimes. We provide a novel analytical method to compute the MFPT, the mean first passage number of resets (MFPNR) and mean first passage entropy (MFPE) in the case where the Brownian particle resets to random positions sampled from a set of distributions known \textit{a priori}. We show the interplay between the reset position distribution's second moment and the reset rate, and the effect it has on the MFPT and MFPE. We further propose a mechanism whereby the entropy per reset can be either in the Maxwell demon or the externally driven regime, yet the overall mean first passage entropy corresponds to the zero-entropy regime. Additionally, we find an overlap between the dynamic phase space and the entropy phase space. We use this method in a generalized version of the Evans-Majumdar model by assuming the reset position is random and sampled from a Gaussian distribution. We then consider the \textit{toggling reset} whereby the Brownian particle resets to a random position sampled from a distribution dependent on the reset parity. All our results are compared to and in agreement with numerical simulations.