Noise sensitivity and variance lower bound for minimal left-right crossing of a square in first-passage percolation

TL;DR

This paper investigates the noise sensitivity and variance lower bounds for minimal left-right crossings in first-passage percolation on a square grid, extending previous results and employing novel probabilistic techniques.

Contribution

It improves noise sensitivity results for passage times in first-passage percolation and establishes new variance lower bounds under curvature assumptions.

Findings

Noise sensitivity for passage times when vertical fluctuations are small.

Extension of noise sensitivity results to larger fluctuation scales under shape assumptions.

Lower bounds on variance of crossing times for certain weight distributions.

Abstract

We study first-passage percolation on $\mathbb Z ^2$ with independent and identically distributed weights, whose common distribution is uniform on $\{a,b\}$ with $0<a<b<\infty $. Following Ahlberg and De la Riva, we consider the passage time $\tau (n,k)$ of the minimal left-right crossing of the square $[0,n]^2$, whose vertical fluctuations are bounded by $k$. We prove that when $k\le n^{1/2-\epsilon}$, the event that $\tau (n,k)$ is larger than its median is noise sensitive. This improves the main result of Ahlberg and De la Riva which holds when $k\le n^{1/22-\epsilon }$. Under the additional assumption that the limit shape is not a polygon with a small number of sides, we extend the result to all $k\le n^{1-\epsilon }$. This extension follows unconditionally when $a$ and $b$ are sufficiently close. Under a stronger curvature assumption, we extend the result to all $k\le n$. This in particular captures the noise sensitivity of the event that the minimal left-right crossing $T_n=\tau (n,n)$ is larger than its median. Finally, under the curvature assumption, our methods give a lower bound of $n^{1/4-\epsilon }$ for the variance of the passage time $T_n$ of the minimal left-right crossing of the square. We prove the last bound also for absolutely continuous weight distributions, generalizing a result of Damron--Houdr\'e--\"Ozdemir, which holds only for the exponential distribution. Our approach differs from the previous works mentioned above; the key idea is to establish a small ball probability estimate in the tail by perturbing the weights for tail events using a Mermin--Wagner type estimate.