Kinetics of self-induced aggregation of Brownian particles: non-Markovian and non-Gaussian features

TL;DR

This paper investigates how non-Markovian and non-Gaussian noise influence the self-induced aggregation of Brownian particles, revealing that noise correlation time significantly affects cluster formation and dynamics.

Contribution

It introduces a model incorporating non-Markovian and non-Gaussian noise into Brownian particle aggregation, highlighting the impact on cluster formation and critical noise thresholds.

Findings

Colored noise induces clustering even at high noise levels

Critical noise strength increases rapidly with noise correlation time, more so for Gaussian noise

Cluster number decreases over time following a power law, with the exponent affected by noise type

Abstract

In this paper we have studied a model for self-induced aggregation in Brownian particle incorporating the non-Markovian and non-Gaussian character of the associated random noise process. In this model the time evolution of each individual is guided by an over-damped Langevin equation of motion with a non-local drift resulting from the local unbalance distributions of the other individuals. Our simulation result shows that colored nose can induce the cluster formation even at large noise strength. Another observation is that critical noise strength grows very rapidly with increase of noise correlation time for Gaussian noise than non Gaussian one. However, at long time limit the cluster number in aggregation process decreases with time following a power law. The exponent in the power law increases remarkable for switching from Markovian to non Markovian noise process.