Is active motion beneficial for target search with resetting in a thermal environment?
Priyo Shankar Pal, Jong-Min Park, Arnab Pal, Hyunggyu Park, Jae Sung, Lee

TL;DR
This study investigates how stochastic resetting influences the search efficiency of an active run-and-tumble particle in a thermal environment, revealing conditions where activity and thermal noise synergistically improve target search times.
Contribution
It introduces a combined analysis of active and thermal noise effects on resetting-based search strategies, highlighting the benefits of activity and velocity reversal in reducing search times.
Findings
Active search outperforms Brownian search with large propulsion and flipping rates.
Thermal noise reduces mean first passage time compared to no thermal noise.
Reversing velocity direction during resetting can further decrease search time.
Abstract
Stochastic resetting has recently emerged as an efficient target-searching strategy in various physical and biological systems. The efficiency of this strategy depends on the type of environmental noise, whether it is thermal or telegraphic (active). While the impact of each noise type on a search process has been investigated separately, their combined effects have not been explored. In this work, we explore the effects of stochastic resetting on an active system, namely a self-propelled run-and-tumble particle immersed in a thermal bath. In particular, we assume that the position of the particle is reset at a fixed rate with or without reversing the direction of self-propelled velocity. Using standard renewal techniques, we compute the mean search time of this active particle to a fixed target and investigate the interplay between active and thermal fluctuations. We find that the…
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Taxonomy
TopicsDiffusion and Search Dynamics · Orbital Angular Momentum in Optics · Infrared Target Detection Methodologies
