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

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 $P[ω> x] \sim x^{-γ}$ as $x \to +\infty$ for some $γ\in (1,2)$. 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 $γ$-stable Lévy white noise. We develop precise and flexible moments estimates adapted to the heavy-tailed setting.

💡 Research Summary

The paper investigates the scaling limits of discrete statistical‑mechanics models when the random environment (disorder) has a heavy‑tailed distribution with infinite second moment. Specifically, the disorder variables ωₓ satisfy a power‑law tail P