Monte Carlo study of the tip region of branching random walks evolved to large times
Anh Dung Le, Alfred H. Mueller, St\'ephane Munier

TL;DR
This study uses Monte Carlo simulations to analyze the tip region of branching random walks at large times, revealing how particle density distributions behave under different constraints and supporting recent theoretical predictions.
Contribution
It introduces an efficient Monte Carlo method to study the tip region of branching Brownian motion at large times, providing numerical evidence for theoretical conjectures.
Findings
Mean and typical particle numbers grow exponentially with elta x when unconstrained.
Typical particle number is suppressed by a factor ^{-\u03b6elta x^{2/3}} under certain constraints.
Numerical results support recent analytical predictions in the infinite-time limit.
Abstract
We implement a discretization of the one-dimensional branching Brownian motion in the form of a Monte Carlo event generator, designed to efficiently produce ensembles of realizations in which the rightmost lead particle at the final time is constrained to have a position larger than some predefined value . The latter may be chosen arbitrarily far from the expectation value of , and the evolution time after which observables on the particle density near the lead particle are measured may be as large as . We then calculate numerically the probability distribution of the number of particles in the interval as a function of . When is significantly smaller than the expectation value of the position of the rightmost lead particle, i.e. when is effectively unconstrained, we check that…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Diffusion and Search Dynamics · Theoretical and Computational Physics
