Sample-path large deviations for stochastic evolutions driven by the square of a Gaussian process

TL;DR

This paper derives explicit sample-path large deviation functionals for stochastic processes driven by quadratic forms of Gaussian processes, revealing noise-induced metastability and enabling analysis of complex dynamical systems.

Contribution

It provides a novel method to compute large deviation rate functions for processes driven by Gaussian quadratic forms using Fredholm determinants and Widom's theorem.

Findings

Explicit large deviation functionals for Gaussian quadratic processes

Identification of noise-induced metastability in a simple model

Construction of instanton trajectories between metastable states

Abstract

Recently, a number of physical models has emerged described by a random process with increments given by a quadratic form of a fast Gaussian process. We find that the rate function which describes sample-path large deviations for such a process can be computed from the large domain size asymptotic of a certain Fredholm determinant. The latter can be evaluated analytically using a theorem of Widom which generalizes the celebrated Szeg\H{o}-Kac formula to the multi-dimensional case. This provides a large class of random dynamical systems with time scale separation for which an explicit sample-path large deviation functional can be found. Inspired by problems in hydrodynamics and atmosphere dynamics, we construct a simple example with a single slow degree of freedom driven by the square of a fast multi-variate Gaussian process and analyse its large deviations functional using our general results. Even though the noiseless limit of this example has a single fixed point, the corresponding large deviations effective potential has multiple fixed points. In other words, it is the addition of noise that leads to metastability. We use the explicit answers for the rate function to construct instanton trajectories connecting the metastable states.