Local Number Fluctuations in Hyperuniform and Nonhyperuniform Systems: Higher-Order Moments and Distribution Functions
The local number variance σ^{2}(R) associated with a spherical sampling window of radius R enables a classification of many-particle systems in d-dimensional Euclidean space R^{d} according to the degree to which large-scale density fluctuations are suppressed, resulting in a demarcation between hyp...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
American Physical Society
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/81498106a5d9484e82f0ecf812a05038 |
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| Sumario: | The local number variance σ^{2}(R) associated with a spherical sampling window of radius R enables a classification of many-particle systems in d-dimensional Euclidean space R^{d} according to the degree to which large-scale density fluctuations are suppressed, resulting in a demarcation between hyperuniform and nonhyperuniform phyla. To more completely characterize density fluctuations, we carry out an extensive study of higher-order moments or cumulants, including the skewness γ_{1}(R), excess kurtosis γ_{2}(R), and the corresponding probability distribution function P[N(R)] of a large family of models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. To carry out this comprehensive program, we derive new theoretical results that apply to general point processes, and we conduct high-precision numerical studies. Specifically, we derive explicit closed-form integral expressions for γ_{1}(R) and γ_{2}(R) that encode structural information up to three-body and four-body correlation functions, respectively. We also derive rigorous bounds on γ_{1}(R), γ_{2}(R), and P[N(R)] for general point processes and corresponding exact results for general packings of identical spheres. High-quality simulation data for γ_{1}(R), γ_{2}(R), and P[N(R)] are generated for each model. We also ascertain the proximity of P[N(R)] to the normal distribution via a novel Gaussian “distance” metric l_{2}(R). Among all models, the convergence to a central limit theorem (CLT) is generally fastest for the disordered hyperuniform processes in two or higher dimensions such that γ_{1}(R)∼l_{2}(R)∼R^{-(d+1)/2} and γ_{2}(R)∼R^{-(d+1)} for large R. The convergence to a CLT is slower for standard nonhyperuniform models and slowest for the “antihyperuniform” model studied here. We prove that one-dimensional hyperuniform systems of class I or any d-dimensional lattice cannot obey a CLT. Remarkably, we discover a type of universality in that, for all of our models that obey a CLT, the gamma distribution provides a good approximation to P[N(R)] across all dimensions for intermediate to large values of R, enabling us to estimate the large-R scalings of γ_{1}(R), γ_{2}(R), and l_{2}(R). For any d-dimensional model that “decorrelates” or “correlates” with d, we elucidate why P[N(R)] increasingly moves toward or away from Gaussian-like behavior, respectively. Our work sheds light on the fundamental importance of higher-order structural information to fully characterize density fluctuations in many-body systems across length scales and dimensions, and thus has broad implications for condensed matter physics, engineering, mathematics, and biology. |
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