Abstract
We consider a model for a directed polymer in a random environment defined on a hierarchical diamond lattice in which i.i.d. random variables are attached to the lattice bonds. Our focus is on scaling schemes in which a size parameter $n$, counting the number of hierarchical layers of the system, becomes large as the inverse temperature $\beta$ vanishes. When $\beta$ has the form $\hat{\beta}/\sqrt{n}$ for a parameter $\hat{\beta} > 0$, we show that there is a cutoff value $0 < \kappa < \infty$ such that as $n \to \infty$ the variance of the normalized partition function tends to zero for $\hat{\beta} \leq \kappa$ and grows without bound for $\hat{\beta} > \kappa$. We obtain a more refined description of the border between these two regimes by setting the inverse temperature to $\kappa/\sqrt{n} + \alpha_n$ where $0 < \alpha_n \ll 1/\sqrt{n}$ and analyzing the asymptotic behavior of the variance. We show that when $\alpha_n = \alpha (\log n - \log \log n)/n^{3/2}$ (with a small modification to deal with non-zero third moment), there is a similar cutoff value $\eta$ for the parameter $\alpha$ such that the variance goes to zero when $\alpha < \eta$ and grows without bound when $\alpha > \eta$. Extending the analysis yet again by probing around the inverse temperature $$$(\kappa/\sqrt{n}) + \eta(\log n - \log \log n)/n^{3/2}$, we find an infinite sequence of nested critical points for the variance behavior of the normalized partition function. In the subcritical cases $\hat{\beta} \leq \kappa$ and $\alpha \leq \eta$, this analysis is extended to a central limit theorem result for the fluctuations of the normalized partition function.
