Invariants of motion with stochastic resetting and space-time coupled returns

Arnab Pal, Łukasz Kuśmierz, Shlomi Reuveni

Research output: Contribution to journalArticlepeer-review

Abstract

Motion under stochastic resetting serves to model a myriad of processes in physics and beyond, but in most cases studied to date resetting to the origin was assumed to take zero time or a time decoupled from the spatial position at the resetting moment. However, in our world, getting from one place to another always takes time and places that are further away take more time to be reached. We thus set off to extend the theory of stochastic resetting such that it would account for this inherent spatio-temporal coupling. We consider a particle that starts at the origin and follows a certain law of stochastic motion until it is interrupted at some random time. The particle then returns to the origin via a prescribed protocol. We study this model and surprisingly discover that the shape of the steady-state distribution which governs the stochastic motion phase does not depend on the return protocol. This shape invariance then gives rise to a simple, and generic, recipe for the computation of the full steady state distribution. Several case studies are analyzed and a class of processes whose steady state is completely invariant with respect to the speed of return is highlighted. For processes in this class we recover the same steady-state obtained for resetting with instantaneous returns - irrespective of whether the actual return speed is high or low. Our work significantly extends previous results on motion with stochastic resetting and is expected to find various applications in statistical, chemical, and biological physics.

Original languageEnglish
Article number113024
JournalNew Journal of Physics
Volume21
Issue number11
DOIs
StatePublished - 13 Nov 2019

Funding

FundersFunder number
Eli Barkai
Tel Aviv University
Azrieli Foundation

    Keywords

    • diffusion with stochastic resetting
    • non-equilibrium steady state
    • restart
    • stochastic processes
    • stochastic resetting

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