On strongly rigid hyperfluctuating random measures

TL;DR

This paper demonstrates that certain hyperfluctuating random measures, specifically hyperplane intersection processes from Poisson hyperplane tessellations, can exhibit strong rigidity, allowing reconstruction of points with minimal information.

Contribution

It provides the first examples of stationary ergodic hyperfluctuating measures that are also strongly rigid, linking hyperfluctuation with a new form of geometric rigidity.

Findings

Hyperplane intersection processes are hyperfluctuating.

These processes exhibit strong rigidity allowing point reconstruction.

Examples connect fluctuations, correlations, and rigidity properties.

Abstract

In contrast to previous belief, we provide examples of stationary ergodic random measures that are both hyperfluctuating and strongly rigid. Therefore, we study hyperplane intersection processes (HIPs) that are formed by the vertices of Poisson hyperplane tessellations. These HIPs are known to be hyperfluctuating, that is, the variance of the number of points in a bounded observation window grows faster than the size of the window. Here we show that the HIPs exhibit a particularly strong rigidity property. For any bounded Borel set $B$, an exponentially small (bounded) stopping set suffices to reconstruct the position of all points in $B$ and, in fact, all hyperplanes intersecting $B$. Therefore, also the random measures supported by the hyperplane intersections of arbitrary (but fixed) dimension, are hyperfluctuating. Our examples aid the search for relations between correlations, density fluctuations, and rigidity properties.