Poisson point process convergence and extreme values in stochastic geometry

Let $\eta_t$ be a Poisson point process with intensity measure $t\mu$, $t>0$, over a Borel space $\mathbb{X}$, where $\mu$ is a fixed measure. Another point process $\xi_t$ on the real line is constructed by applying a symmetric function $f$ to every $k$-tuple of distinct points of $\eta_t$. It is shown that $\xi_t$ behaves after appropriate rescaling like a Poisson point process, as $t\to\infty$, under suitable conditions on $\eta_t$ and $f$. This also implies Weibull limit theorems for related extreme values. The result is then applied to investigate problems arising in stochastic geometry, including small cells in Voronoi tessellations, random simplices generated by non-stationary hyperplane processes, triangular counts with angular constraints and non-intersecting $k$-flats. Similar results are derived if the underlying Poisson point process is replaced by a binomial point process.