Fluctuating Fronts as Correlated Extreme Value Problems: An Example of Gaussian Statistics

TL;DR

This paper models fluctuating particle fronts as correlated extreme value problems and demonstrates that the distribution of the front's maximum position is Gaussian in a fermionic system, with small deviations in a bosonic system.

Contribution

It introduces a novel perspective by framing fluctuating fronts as correlated extreme value problems and provides analytical results for their distribution.

Findings

Gaussian distribution for fermionic front models

Small deviations from Gaussian in bosonic models

Highlights importance of correlations in extreme value statistics

Abstract

In this paper, we view fluctuating fronts made of particles on a one-dimensional lattice as an extreme value problem. The idea is to denote the configuration for a single front realization at time $t$ by the set of co-ordinates $\{k_i(t)\}\equiv[k_1(t),k_2(t),...,k_{N(t)}(t)]$ of the constituent particles, where $N(t)$ is the total number of particles in that realization at time $t$. When $\{k_i(t)\}$ are arranged in the ascending order of magnitudes, the instantaneous front position can be denoted by the location of the rightmost particle, i.e., by the extremal value $k_f(t)=\text{max}[k_1(t),k_2(t),...,k_{N(t)}(t)]$. Due to interparticle interactions, $\{k_i(t)\}$ at two different times for a single front realization are naturally not independent of each other, and thus the probability distribution $P_{k_f}(t)$ [based on an ensemble of such front realizations] describes extreme value statistics for a set of correlated random variables. In view of the fact that exact results for correlated extreme value statistics are rather rare, here we show that for a fermionic front model in a reaction-diffusion system, $P_{k_f}(t)$ is Gaussian. In a bosonic front model however, we observe small deviations from the Gaussian.