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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.15009 |
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| _version_ | 1866917320430977024 |
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| author | Liu, Yuan Li, Xurui Tian, Jianxiang Yan, Xunwang Zhang, Ge |
| author_facet | Liu, Yuan Li, Xurui Tian, Jianxiang Yan, Xunwang Zhang, Ge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of Random Spatial Truncation and Reciprocal-Space Binning on the Detection of Hyperuniformity in Disordered Systems Liu, Yuan Li, Xurui Tian, Jianxiang Yan, Xunwang Zhang, Ge Disordered Systems and Neural Networks 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. |
| title | Impact of Random Spatial Truncation and Reciprocal-Space Binning on the Detection of Hyperuniformity in Disordered Systems |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2511.15009 |