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Luca Lotz

Publications and source records attributed to Luca Lotz.

2 recordsLinked to original sources

Persistence of asymptotic variance under transport: from hyperfluctuation to stealthy hyperuniformity

We introduce $p$-uniformity to characterize the scaling of density fluctuations in spatial random systems in $\mathbb R^d$, ranging from hyperfluctuation to stealthy hyperuniformity. Our central theorem establishes sufficient conditions to preserve $p$-uniformity under transport. The first condition, a finite $(d+p)$-th moment of the transport distance, allows for a Taylor expansion of the transport. The second condition controls the corresponding terms. We thus solve a previously stated open problem; indeed we extend it, since our result applies to a general $p$-uniform source in any dimension, and the source and transport may be dependent. As an application, we construct new classes of point processes that are isotropic and $p$-uniform with arbitrarily high $p$, and that can be simulated in linear time. We thus achieve three-dimensional isotropic hyperuniform samples with an unprecedented system size of $10^9$ points. We conclude with an outlook on a converse statement.

math.PR

Invariant transports of stationary random measures: asymptotic variance, hyperuniformity, and examples

We consider invariant transports of stationary random measures on $\mathbb{R}^d$ and establish natural mixing criteria that guarantee persistence of asymptotic variances. To check our mixing assumptions, which are based on two-point Palm probabilities, we combine factorial moment expansion with stopping set techniques, among others. We complement our results by providing formulas for the Bartlett spectral measure of the destinations. We pay special attention to the case of a vanishing asymptotic variance, known as hyperuniformity. By constructing suitable transports from a hyperuniform source we are able to rigorously establish hyperuniformity for many point processes and random measures. On the other hand, our method can also refute hyperuniformity. For instance, we show that finitely many steps of Lloyd's algorithm or of a random organization model preserve the asymptotic variance if we start from a Poisson process or a point process with exponentially fast decaying correlation. Finally, we define a hyperuniformerer that turns any ergodic point process with finite intensity into a hyperuniform process by randomizing each point within its cell of a fair partition.

math.PR