Cover times of many diffusive or subdiffusive searchers

TL;DR

This paper derives a universal formula for the moments of cover times in systems with many diffusive or subdiffusive searchers, revealing that prior simple rescaling estimates are inaccurate as the number of searchers increases.

Contribution

It introduces a new universal formula for cover times of multiple searchers, extending beyond previous single-searcher estimates and accounting for many searcher interactions.

Findings

Prior estimates break down with many searchers.

Derived a universal formula depending on diffusivity and geodesic distance.

Validated results with stochastic simulations.

Abstract

Cover times measure the speed of exhaustive searches which require the exploration of an entire spatial region(s). Applications include the immune system hunting pathogens, animals collecting food, robotic demining or cleaning, and computer search algorithms. Mathematically, a cover time is the first time a random searcher(s) comes within a specified "detection radius" of every point in the target region (often the entire spatial domain). Due to their many applications and their fundamental probabilistic importance, cover times have been extensively studied in the physics and probability literatures. This prior work has generally studied cover times of a single searcher with a vanishing detection radius or a large target region. This prior work has further claimed that cover times for multiple searchers can be estimated by a simple rescaling of the cover time of a single searcher. In this paper, we study cover times of many diffusive or subdiffusive searchers and show that prior estimates break down as the number of searchers grows. We prove a rather universal formula for all the moments of such cover times in the many searcher limit that depends only on (i) the searcher's characteristic (sub)diffusivity and (ii) a certain geodesic distance between the searcher starting location(s) and the farthest point in the target. This formula is otherwise independent of the detection radius, space dimension, target size, and domain size. We illustrate our results in several examples and compare them to detailed stochastic simulations.