Abstract
The aim of the present article is to introduce a concept which allows to generalise the notion of Poissonian pair correlation, a second-order equidistribution property, to higher dimensions. Roughly speaking, in the one-dimensional setting, the pair correlation statistics measures the distribution of spacings between sequence elements in the unit interval at distances of order of the mean spacing 1 / N. In the d-dimensional case, of course, the order of the mean spacing is 1/N^{frac{1}{d}}, and—in our concept—the distance of sequence elements will be measured by the supremum-norm. Additionally, we show that, in some sense, almost all sequences satisfy this new concept and we examine the link to uniform distribution. The metrical pair correlation theory is investigated and it is proven that a class of typical low-discrepancy sequences in the high-dimensional unit cube do not have Poissonian pair correlations, which fits the existing results in the one-dimensional case.
Highlights
Introduction and statement of resultsThe concept of Poissonian pair correlations has its origin in quantum mechanics, where the spacings of energy levels of integrable systems were studied
We indicate by bold symbols that we work with d-dimensional vectors of real numbers or random variables
We say that a sequencen∈N ∈ [0, 1)d has Poissonian pair correlations if the multi-dimensional pair correlation statistics
Summary
The author is supported by the Austrian Science Fund (FWF) Project F5509-N26, which is a part of the Special Research Program “Quasi-Monte Carlo Methods: Theory and Applications”. L. Kaltenböck, W. Stockinger, G. Larcher: The author is supported by the Austrian Science Fund (FWF), Project F5507-N26, which is a part of the Special Research Program “Quasi-Monte Carlo Methods: Theory and Applications” and Project I1751-N26.
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