Dichotomous flow with thermal diffusion and stochastic resetting

TL;DR

This paper investigates how stochastic resetting and resonant activation influence one-dimensional diffusive dichotomous flow, revealing distinct mechanisms of optimization and their limitations in symmetric systems.

Contribution

It provides a comparative analysis of stochastic resetting and resonant activation, highlighting their different origins and effects on noise-induced escape optimization.

Findings

Resetting eliminates suboptimal trajectories, enhancing efficiency.

Resonant activation matches time scales to optimize escape.

Resetting does not significantly improve resonant activation in symmetric setups.

Abstract

We consider properties of one-dimensional diffusive dichotomous flow and discuss effects of resonant activation in the presence of statistically independent random resetting mechanism. Resonant activation and stochastic resetting are two similar effects, as both of them can optimize the noise induced escape. Our studies show completely different origins of optimization in adapted setups. Efficiency of stochastic resetting relies on elimination of suboptimal trajectories while resonant activation is associated with matching of time scales in the dynamic environment. Consequently, both effects can be easily tracked by studying their asymptotic properties. Finally, stochastic resetting cannot be easily used to further optimization of the resonant activation in symmetric setups.