Tracer dynamics in the active random average process
Saikat Santra, Prashant Singh, and Anupam Kundu

TL;DR
This paper analyzes how non-Markovian active dynamics influence tracer particle fluctuations in a one-dimensional random average process, revealing sub-diffusive growth and specific ratio relations between different initial conditions.
Contribution
It introduces a model with active, persistent motion in the RAP, deriving analytical results for variance growth and correlations, and confirms findings through simulations.
Findings
Variance of tracer position grows as √t with different coefficients for initial conditions.
Ratio of coefficients for annealed and quenched initial conditions is √2, consistent with other systems.
Active persistence affects higher-order correlations, altering fluctuation behavior.
Abstract
We investigate the dynamics of tracer particles in the random average process (RAP), a single-file system in one dimension. In addition to the position, every particle possesses an internal spin variable that can alternate between two values, , at a constant rate . Physically, the value of dictates the direction of motion of the corresponding particle and for finite , every particle performs a non-Markovian active dynamics. Herein, we study the effect of this non-Markovianity in the fluctuations and correlations of the positions of tracer particles. We analytically show that the variance of the position of a tagged particle grows sub-diffusively as at large times for the quenched uniform initial condition. While this sub-diffusive growth is identical to that of the Markovian/non-persistent RAP, the…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Theoretical and Computational Physics · Advanced Thermodynamics and Statistical Mechanics
