The stationary horizon as the central multi-type invariant measure in the KPZ universality class
Evan Sorensen

TL;DR
This paper studies the stationary horizon (SH), a key invariant measure in the KPZ universality class, providing new constructions, distribution formulas, and demonstrating its universality and role as the unique invariant distribution for the directed landscape.
Contribution
It offers an alternative construction of the SH, derives exact distribution formulas, and establishes its universality and uniqueness as the invariant measure in the KPZ class.
Findings
SH is the unique coupled invariant distribution for the directed landscape
SH describes the Busemann process for the DL
SH appears as the scaling limit of multi-species invariant measures in TASEP
Abstract
The Kardar-Parisi-Zhang (KPZ) universality class describes a large class of 2-dimensional models of random growth, which exhibit universal scaling exponents and limiting statistics. The last ten years has seen remarkable progress in this area, with the formal construction of two interrelated limiting objects, now termed the KPZ fixed point and the directed landscape (DL). This dissertation focuses on a third central object, termed the stationary horizon (SH). The SH was first introduced (and named) by Busani as the scaling limit of the Busemann process in exponential last-passage percolation. Shortly after, in the author's joint work with Sepp\"al\"ainen, it was independently constructed in the context of Brownian last-passage percolation. In this dissertation, we give an alternate construction of the SH, directly from the description of its finite-dimensional distributions and without…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and statistical mechanics · Random Matrices and Applications · Theoretical and Computational Physics
