Extreme first passage times of piecewise deterministic Markov processes

The time it takes the fastest searcher out of N 1 searchers to find a target determines the timescale of many physical, chemical, and biological processes. This time is called an extreme first passage time (FPT) and is typically much faster than the FPT of a single searcher. Extreme FPTs of diffusion have been studied for decades, but little is known for other types of stochastic processes. In this paper, we study the distribution of extreme FPTs of piecewise deterministic Markov processes (PDMPs). PDMPs are a broad class of stochastic processes that evolve deterministically between random events. Using classical extreme value theory, we prove general theorems which yield the distribution and moments of extreme FPTs in the limit of many searchers based on the short time distribution of the FPT of a single searcher. We then apply these theorems to some canonical PDMPs, including run and tumble searchers in one, two, and three space dimensions. We discuss our results in the context of some biological systems and show how our approach accounts for an unphysical property of diffusion which can be problematic for extreme statistics.

Automated summary

The paper investigates the distribution of extreme first passage times of piecewise deterministic Markov processes (PDMPs) by using classical extreme value theory to prove general theorems for the distribution and moments of extreme FPTs in the limit of many searchers. The results are then applied to canonical PDMPs, including run and tumble searchers in different dimensions, and discussed in the context of biological systems. The approach also addresses an unphysical property of diffusion that can be problematic for extreme statistics.