Disordered systems and (subcritical) polynomial chaos with heavy-tail disorder

Abstract

We study discrete statistical mechanics systems perturbed by a random environment without a finite second moment. Specifically, we consider a random environment whose tail distribution satisfies $P[\ddot{I}‰>x]\sim x^{-{\hat{I}}^3}$ as $x\to +\infty$ for some ${\hat{I}}^3\in (1,2)$. Inspired by the seminal work of Caravenna, Sun and Zygouras \cite{csz_2016}, we adopt a general framework that encompasses as key examples both the disordered pinning model and the long-range directed polymer model. We provide some subcriticality condition under which we prove that the discrete disordered system possesses a non-trivial scaling limit. We also interpret the subcriticality condition in terms of a generalized Harris criterion without second moment, which gives a prediction for disorder relevance depending on the parameters of the system. Our analysis relies on the study of multilinear polynomials of independent heavy-tailed random variables known as polynomial chaos and their continuous analogue, given by multiple integrals with respect to a ${\hat{I}}^3$-stable Lévy white noise. We develop precise and flexible moments estimates adapted to the heavy-tailed setting.