Target Search by Active Particles

Active particles, which are self-propelled nonequilibrium systems, are modeled by overdamped Langevin equations with colored noise, emulating the self-propulsion. In this chapter, we present a review of the theoretical results for the target search problem of these particles. We focus on the three best known models, namely, run-and-tumble particles, active Brownian particles, and direction reversing active Brownian particles, which differ in their self-propulsion dynamics. For each of these models, we discuss the first-passage and survival probabilities in the presence of an absorbing target. We also discuss how resetting helps the active particles find targets in a finite time.