Effects of mortality on stochastic search processes with resetting

TL;DR

This paper investigates how stochastic resetting affects the first-passage time of a mortal Brownian particle, revealing conditions under which resetting can optimize the probability of reaching a target before death.

Contribution

It provides exact analytical expressions for first-passage times with mortality and resetting, and analyzes the complex interplay between mortality rate and resetting in optimizing search efficiency.

Findings

Existence of a resetting rate that maximizes reach probability when lifetime is long enough.

Identification of a resetting rate that minimizes mean first-passage time, distinct from the probability maximum.

Resetting can both enhance and hinder search success depending on mortality and diffusion parameters.

Abstract

We study the first-passage time to the origin of a mortal Brownian particle, with mortality rate $ \mu $, diffusing in one dimension. The particle starts its motion from $ x>0 $ and it is subject to stochastic resetting with constant rate $ r $. We first unveil the relation between the probability of reaching the target and the mean first-passage time of the corresponding problem in absence of mortality, which allows us to deduce under which conditions the former can be increased by adjusting the restart rate. We then consider the first-passage time conditioned on the event that the particle reaches the target before dying, and provide exact expressions for the mean and the variance as functions of $ r $, corroborated by numerical simulations. By studying the impact of resetting for different mortality regimes, we also show that, if the average lifetime $ \tau_\mu=1/\mu $ is long enough with respect to the diffusive time scale $ \tau_D=x^2/(4D) $, there exist both a resetting rate $ r_\mu^* $ that maximizes the probability and a rate $ r_m $ that minimizes the mean first-passage time. However, the two never coincide for positive $ \mu $, making the optimization problem highly nontrivial.