English

Shrinking targets versus recurrence: the quantitative theory

Dynamical Systems 2024-10-31 v1

Abstract

Let X=[0,1]X = [0,1], and let T:XXT:X\to X be an expanding piecewise linear map sending each interval of linearity to [0,1][0,1]. For ψ:NR0\psi:\mathbb N\to\mathbb R_{\geq 0}, xXx\in X, and NNN\in\mathbb N we consider the recurrence counting function R(x,N;T,ψ):=#{1nN:d(Tnx,x)<ψ(n)}. R(x,N;T,\psi) := \#\{1\leq n\leq N: d(T^n x, x) < \psi(n)\}. We show that for any ε>0\varepsilon > 0 we have R(x,N;T,ψ)=Ψ(N)+O(Ψ1/2(N) (logΨ(N))3/2+ε) R(x,N;T,\psi) = \Psi(N)+O\left(\Psi^{1/2}(N) \ (\log\Psi(N))^{3/2+\varepsilon}\right) for μ\mu-almost all xXx\in X and for all NNN\in\mathbb N, where Ψ(N):=2n=1Nψ(n)\Psi(N):= 2 \sum_{n=1}^N \psi(n). We also prove a generalization of this result to higher dimensions.

Keywords

Cite

@article{arxiv.2410.22993,
  title  = {Shrinking targets versus recurrence: the quantitative theory},
  author = {Jason Levesley and Bing Li and David Simmons and Sanju Velani},
  journal= {arXiv preprint arXiv:2410.22993},
  year   = {2024}
}
R2 v1 2026-06-28T19:41:09.548Z