Last-passage time for linear diffusions and application to the emptying time of a box

TL;DR

This paper analyzes the last-passage times for linear diffusions, explores spectral properties of associated Schrödinger operators, and applies findings to the emptying time of a particle system, revealing connections to extreme value statistics.

Contribution

It provides explicit formulas for last-passage times, relates dual diffusions via spectral analysis, and applies these results to out-of-equilibrium particle systems.

Findings

Laplace transform of last-passage time density derived

Explicit formulas for mean last-passage time obtained

Limiting distribution of emptying time is Gumbel

Abstract

We study the statistics of last-passage time for linear diffusions. First we present an elementary derivation of the Laplace transform of the probability density of the last-passage time, thus recovering known results from the mathematical literature. We then illustrate them on several explicit examples. In a second step we study the spectral properties of the Schr\"{o}dinger operator associated to such diffusions in an even potential $U(x) = U(-x)$, unveiling the role played by the so-called Weyl coefficient. Indeed, in this case, our approach allows us to relate the last-passage times for dual diffusions (i.e., diffusions driven by opposite force fields) and to obtain new explicit formulae for the mean last-passage time. We further show that, for such even potentials, the small time $t$ expansion of the mean last-passage time on the interval $[0,t]$ involves the Korteveg-de Vries invariants, which are well known in the theory of Schr\"odinger operators. Finally, we apply these results to study the emptying time of a one-dimensional box, of size $L$, containing $N$ independent Brownian particles subjected to a constant drift. In the scaling limit where both $N \to \infty$ and $L \to \infty$, keeping the density $\rho = N/L$ fixed, we show that the limiting density of the emptying time is given by a Gumbel distribution. Our analysis provides a new example of the applications of extreme value statistics to out-of-equilibrium systems.