# A giant disparity and a dynamical phase transition in large deviations of the time-averaged size of stochastic populations

## TL;DR

This paper analyzes large deviations in stochastic populations, revealing a giant disparity in probabilities of extreme population sizes and identifying a second-order dynamical phase transition through advanced WKB methods.

## Contribution

It introduces a novel WKB-based approach combined with large deviation formalism to characterize a giant disparity and a dynamical phase transition in population size fluctuations.

## Key findings

- Giant disparity between small and large population deviations.
- Identification of a second-order dynamical phase transition.
- Finite N smoothing captured by van-Kampen expansion.

## Abstract

We study large deviations of the time-averaged size of stochastic populations described by a continuous-time Markov jump process. When the expected population size $N$ in the steady state is large, the large deviation function (LDF) of the time-averaged population size can be evaluated by using a WKB (after Wentzel, Kramers and Brillouin) method, applied directly to the master equation for the Markov process. For a class of models that we identify, the direct WKB method predicts a giant disparity between the probabilities of observing an unusually small and an unusually large values of the time-averaged population size. The disparity results from a qualitative change in the "optimal" trajectory of the underlying classical mechanics problem. The direct WKB method also predicts, in the limit of $N\to \infty$, a singularity of the LDF, which can be interpreted as a second-order dynamical phase transition. The transition is smoothed at finite $N$, but the giant disparity remains. The smoothing effect is captured by the van-Kampen system size expansion of the exact master equation near the attracting fixed point of the underlying deterministic model. We describe the giant disparity at finite $N$ by developing a different variant of WKB method, which is applied in conjunction with the Donsker-Varadhan large-deviation formalism and involves subleading-order calculations in $1/N$.