Brownian bridge limit of path measures in the upper tail of KPZ models

TL;DR

This paper proves that the path measures in KPZ models' upper tail converge to a Brownian bridge, using geometric and probabilistic methods, and provides new insights into polymer measure structure under upper tail conditioning.

Contribution

It establishes the Brownian bridge limit for KPZ path measures in both zero and positive temperatures without relying on exact formulas, introducing novel coalescence estimates and probabilistic techniques.

Findings

Path measures converge to Brownian bridges in upper tail regimes.

New coalescence estimates for KPZ models.

Insights into polymer measure structure under upper tail conditioning.

Abstract

For models in the KPZ universality class, such as the zero temperature model of planar last passage-percolation (LPP) and the positive temperature model of directed polymers, its upper tail behavior has been a topic of recent interest, with particular focus on the associated path measures (i.e., geodesics or polymers). For Exponential LPP, diffusive fluctuation had been established in Basu-Ganguly. In the directed landscape, the continuum limit of LPP, the limiting Gaussianity at one point, as well as of related finite-dimensional distributions of the KPZ fixed point, were established, using exact formulas in Liu and Wang-Liu. It was further conjectured in these works that the limit of the corresponding geodesic should be a Brownian bridge. We prove it in both zero and positive temperatures; for the latter, neither the one-point limit nor the scale of fluctuations was previously known. Instead of relying on formulas (which are still missing in the positive temperature literature), our arguments are geometric and probabilistic, using the results on the shape of the weight and free energy profiles under the upper tail from Ganguly-Hegde as a starting point. Another key ingredient involves novel coalescence estimates, developed using the recently discovered shift-invariance Borodin-Gorin-Wheeler in these models. Finally, our proof also yields insight into the structure of the polymer measure under the upper tail conditioning, establishing a quenched localization exponent around a random backbone.