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Poissonian Pair Correlation in Higher Dimensions

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abstract

Let $(x_n)_{n=1}^{\infty}$ be a sequence on the torus $\mathbb{T}$ (normalized to length 1). A sequence $(x_n)$ is said to have Poissonian pair correlation if, for all $s>0$, $$ \lim_{N \rightarrow \infty}{ \frac{1}{N} \# \left\{ 1 \leq m \neq n \leq N: |x_m - x_n| \leq \frac{s}{N} \right\}} = 2s.$$ It is known that this implies uniform distribution of the sequence $(x_n)$. Hinrichs, Kaltenb\"ock, Larcher, Stockinger \& Ullrich extended this result to higher dimensions and showed that sequences $(x_n)$ in $[0,1]^d$ that satisfy, for all $s>0$, $$ \lim_{N \rightarrow \infty}{ \frac{1}{N} \# \left\{ 1 \leq m \neq n \leq N: \|x_m - x_n\|_{\infty} \leq \frac{s}{N} \right\}} = (2s)^d.$$ are also uniformly distributed. We prove the same result for the extension by the Euclidean norm: if a sequence $(x_n)$ in $\mathbb{T}^d$ satisfies, for all $s > 0$, $$ \lim_{N \rightarrow \infty}{ \frac{1}{N} \# \left\{ 1 \leq m \neq n \leq N: \|x_m - x_n\|_{2} \leq \frac{s}{N} \right\}} = \omega_d s^d$$ where $\omega_d$ is the volume of the unit ball, then $(x_n)$ is uniformly distributed. Our approach shows that Poissonian Pair Correlation implies an exponential sum estimate that resembles and implies the Weyl criterion.

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math.NT 1

years

2019 1

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CONDITIONAL 1

representative citing papers

On the number of gaps of sequences with Poissonian Pair Correlations

math.NT · 2019-08-17 · conditional · novelty 7.0

For any slowly growing function f, there exists a Poissonian pair correlation sequence whose number of distinct neighboring gap lengths is at most f(n), and for every such sequence the maximal gap multiplicity is o(n).

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  • On the number of gaps of sequences with Poissonian Pair Correlations math.NT · 2019-08-17 · conditional · none · ref 14 · internal anchor

    For any slowly growing function f, there exists a Poissonian pair correlation sequence whose number of distinct neighboring gap lengths is at most f(n), and for every such sequence the maximal gap multiplicity is o(n).