Impact of Random Spatial Truncation and Reciprocal-Space Binning on the Detection of Hyperuniformity in Disordered Systems

Abstract

We study how finite-window sampling (random spatial truncation) and reciprocal-space radial binning influence the detection of hyperuniformity in disordered systems. Using thirteen representative two-dimensional simulation systems (two stealthy hyperuniform systems with distinct constraint parameters and ; hyperuniform Gaussian pair statistics system; six hyperuniform targeted systems with distinct alpha=0.5, 0.7, 1.0, 1.3, 1.5, 3.0, random sequential addition system; Poisson points distribution system; Lennard-Jones fluid system and Yukawa fluid system) and two real biological systems (avian photoreceptor patterns and looped leaf vein networks) We find that moderate random spatial truncation (i.e., randomly extracting a smaller subwindow from the original full-field configuration) does not change qualitatively the hyperuniformity classification of the systems. Specifically, disordered hyperuniform systems retain their respective hyperuniformity classes despite a modest reduction in measured hyperuniformity exponent alpha (i.e., reduction in small-k suppression). Moreover, spatial truncation commonly induces configuration-dependent fluctuations of small-k values of S(k). We show that modest reciprocal-space radial pooling (controlled by a binning parameter m) effectively smooths such spurious wiggles without changing the hyperuniformity class. Practical guidelines for choosing m, cross-checking spectral fits with the local number variance scaling, and increasing effective sampling are provided. These results provide concrete, low-cost and effective methodology for robust spectral detection of hyperuniformity in finite and truncated datasets which abound in experimental systems.