Scaling limit of 1+1 dimensional directed polymer with power-law tail and spatial correlated noise

Abstract

We study a $(1+1)$-dimensional directed polymer in a spatially correlated random environment generated by power-law tail variables: $ω(i,x)=\sum_{y\in\mathbb Z}ψ_{y-x}ξ(i,y), ψ_y\sim λ_r |y|^{-r}, r\in(1/2,1)$, where the variables $ξ(i,y)$ are i.i.d. and have a regularly varying right tail with exponent $α>2$. The spatial covariance of the environment has long-range decay with Hurst parameter $H=\frac32-r\in(1/2,1)$. We identify the limiting fluctuations of the log-partition function in the intermediate disorder regime and show that the critical tail exponent is $α_c=\frac{3}{H}=\frac{6}{3-2r}$. When $α>α_c$, the model has the same scaling limits as the corresponding Gaussian spatially correlated polymer: if $β_NN^{H/2}\toβ\in(0,\infty)$, the centered log-partition function converges to the logarithm of the solution of the stochastic heat equation driven by fractional spatial noise; if $β_NN^{H/2}\to0$, its normalized fluctuation converges to a centered Gaussian law. In the regime $2<α\leα_c$, at the scale $β_NN^{H/2}=βN^{H/2}/l(N^{3/2})$, the log-partition function still satisfies Gaussian fluctuation. The main ingredient is a truncation comparison argument adapted to long-range moving-average environments, together with an invariance principle for polynomial chaos. Due to the non-locality of the environments, we perform a far-near field analysis, as well as multiscale analysis, to prove that the truncated version does not change the log-partition function at the corresponding scales.