Exceptional times when the KPZ fixed point violates Johansson's conjecture on maximizer uniqueness
Ivan Corwin, Alan Hammond, Milind Hegde, Konstantin Matetski

TL;DR
This paper investigates the rare events where the KPZ fixed point's spatial maximum is achieved at multiple locations, extending known uniqueness results and quantifying the probability and structure of such maximizer non-uniqueness over time.
Contribution
It extends maximizer uniqueness results of the KPZ fixed point to broader initial data and analyzes the probability and structure of non-uniqueness events over time.
Findings
Maximizer non-uniqueness occurs at times with Hausdorff dimension at most two-thirds.
Quantitative bounds on the probability of multiple near-maximizers.
Derived a joint density-like quantity for maximizer locations and maximum value.
Abstract
In 2002, Johansson conjectured that the maximum of the Airy process minus the parabola is almost surely achieved at a unique location. This result was proved a decade later by Corwin and Hammond; Moreno Flores, Quastel and Remenik; and Pimentel. Up to scaling, the Airy process minus the parabola arises as the fixed time spatial marginal of the KPZ fixed point when started from narrow wedge initial data. We extend this maximizer uniqueness result to the fixed time spatial marginal of the KPZ fixed point when begun from any element of a very broad class of initial data. None of these results rules out the possibility that at random times, the KPZ fixed point spatial marginal violates maximizer uniqueness. To understand this possibility, we study the probability that the KPZ fixed point has, at a given time, two or more locations where its value is close to the…
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
TopicsPoint processes and geometric inequalities · Stochastic processes and statistical mechanics · Bayesian Methods and Mixture Models
