From symmetry break to Poisson point process in 2D Voronoi tessellations: the generic nature of hexagons
Valerio Lucarini

TL;DR
This paper investigates how symmetry-breaking noise influences 2D Voronoi tessellations, revealing the stability of hexagons and their statistical properties as the system transitions from regular to Poisson distributions.
Contribution
It introduces a unified framework analyzing symmetry break and randomness in Voronoi tessellations, highlighting the robustness of hexagons and their statistical behavior under Gaussian noise.
Findings
Hexagons dominate under small noise levels (alpha<0.1).
Statistics converge to Poisson-Voronoi results for alpha>2.
The perimeter of cells follows a square root law with respect to the number of sides.
Abstract
We bridge the properties of the regular square and honeycomb Voronoi tessellations of the plane to those of the Poisson-Voronoi case, thus analyzing in a common framework symmetry-break processes and the approach to uniformly random distributions of tessellation-generating points. We consider ensemble simulations of tessellations generated by points whose regular positions are perturbed through a Gaussian noise controlled by the parameter alpha. We study the number of sides, the area, and the perimeter of the Voronoi cells. For alpha>0, hexagons are the most common class of cells, and 2-parameter gamma distributions describe well the statistics of the geometrical characteristics. The symmetry break due to noise destroys the square tessellation, whereas the honeycomb hexagonal tessellation is very stable and all Voronoi cells are hexagon for small but finite noise with alpha<0.1. For a…
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