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

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 $\ddot{I}„(n,k)$ of the minimal left-right crossing of the square $[0,n{]}^2$, whose vertical fluctuations are bounded by $k$. We prove that when $k\le n^{1/2-\hat{I}\mu }$, the event that $\ddot{I}„(n,k)$ is larger than its median is noise sensitive. This improves the main result of Ahlberg and De la Riva which holds when $k\le n^{1/22-\hat{I}\mu }$. Under the additional assumption that the limit shape is not a polygon with a small number of sides, we extend the result to all $k\le n^{1-\hat{I}\mu }$. 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 $T_n=\ddot{I}„(n,n)$ is larger than its median. Finally, under the curvature assumption, our methods give a lower bound of $n^{1/4-\hat{I}\mu }$ 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--Houdré--Özdemir, 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.