Point process convergence of extremes in $K$-symmetric exclusion

Abstract

We consider the behavior of extremal particles in $K$-symmetric exclusion on $\mathbb{Z}$ when the process starts from certain infinite-particle step configurations where there are no particles to the right of a maximal one. In such a system, the occupancy of a site is limited to at most $K \geq 1$. Let $X^{(0)}_t\geq X^{(1)}_t\geq \cdots$ denote the order statistics of the particles in the system. We show that the point process $\sum_{m=0}^\infty δ_{v_t(X_{t/K}^{(m)})}$ converges in distribution as $t \to \infty$ to a Poisson random measure on $\mathbb{R}$ with intensity proportional to $e^{-x}\,dx$, where $v_t(x) = (σb_t)^{-1}x - a_t$, $a_t = \log(t/ (\sqrt{2π} \log t))$, $b_t = (t/\log t)^{1/2}$, and $σ$ is the standard deviation of the random walk jump probabilities. From this limit, we further deduce the asymptotic joint distributions for the extreme statistics and the spacings between them. Moreover, to probe effects of the number of particles on the behavior of the extremes, we consider an array of truncated step profiles supported on blocks of $L(t)$ sites at times $t\geq 0$. Letting $L(t) \to \infty$ with $t \to \infty$, we obtain Poisson random measure limits in different scaling regimes determined by $L(t)$. These results show robustness of both previously known and newly introduced superdiffusive scaling limits for the extremes in the symmetric exclusion process ($K=1$) by extending them to the larger class of $K\geq 2$ exclusion. Furthermore, proofs are more general than previously known techniques, relying on moment bounds and a semigroup monotonicity estimate to control particle correlations.