# Living on the edge of instability

## TL;DR

This paper analyzes the statistical behavior of trajectories in highly unstable potentials, distinguishing between diverging and non-diverging paths, and introduces two limit distributions that generalize Gibbs distribution for such systems.

## Contribution

It introduces a novel framework using Q-processes and quasi-stationary distributions to describe non-diverging trajectories in unstable potentials, revealing fundamentally different distribution properties.

## Key findings

- Distribution for diverging trajectories is light-tailed and localized.
- Distribution for non-diverging trajectories is heavy-tailed with a power-law decay.
- Results apply broadly to nonlinear dynamical models with meta-stable states.

## Abstract

Statistical description of stochastic dynamics in highly unstable potentials is strongly affected by properties of divergent trajectories, that quickly leave meta-stable regions of the potential landscape and never return. Using ideas from theory of Q-processes and quasi-stationary distributions, we analyze position statistics of non-diverging trajectories. We discuss two limit distributions which can be considered as (formal) generalizations of the Gibbs canonical distribution to highly unstable systems. Even though the associated effective potentials differ only slightly, properties of the two distributions are fundamentally different for all highly unstable system. The distribution for trajectories conditioned to diverge in an infinitely distant future is localized and light-tailed. The other distribution, describing trajectories surviving in the meta-stable region at the instant of conditioning, is heavy-tailed. The exponent of the corresponding power-law tail is determined by the leading divergent term of the unstable potential. We discuss different equivalent forms of the two distributions and derive properties of the effective statistical force arising in the ensemble of non-diverging trajectories after the Doob h-transform. The obtained explicit results generically apply to non-linear dynamical models with meta-stable states and fast kinetic transitions.