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Shrinking parallelepiped targets for $\beta $-dynamical systems

Part of: Classical measure theory Probabilistic theory: distribution modulo $1$; metric theory of algorithms

Published online by Cambridge University Press:  19 November 2024

YUBIN HE
YUBIN HE*
Affiliation:
Department of Mathematics, Shantou University, Shantou, Guangdong 515063, China
*
e-mail: ybhe@stu.edu.cn
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Abstract

For $ \beta>1 $, let $ T_\beta $ be the $\beta $-transformation on $ [0,1) $. Let $ \beta _1,\ldots ,\beta _d>1 $ and let $ \mathcal P=\{P_n\}_{n\ge 1} $ be a sequence of parallelepipeds in $ [0,1)^d $. Define

$$ \begin{align*}W(\mathcal P)=\{\mathbf{x}\in[0,1)^d:(T_{\beta_1}\times\cdots \times T_{\beta_d})^n(\mathbf{x})\in P_n\text{ infinitely often}\}.\end{align*} $$
When each $ P_n $ is a hyperrectangle with sides parallel to the axes, the ‘rectangle to rectangle’ mass transference principle by Wang and Wu [Mass transference principle from rectangles to rectangles in Diophantine approximation. Math. Ann. 381 (2021) 243–317] is usually employed to derive the lower bound for $\dim _{\mathrm {H}} W(\mathcal P)$, where $\dim _{\mathrm {H}}$ denotes the Hausdorff dimension. However, in the case where $ P_n $ is still a hyperrectangle but with rotation, this principle, while still applicable, often fails to yield the desired lower bound. In this paper, we determine the optimal cover of parallelepipeds, thereby obtaining $\dim _{\mathrm {H}} W(\mathcal P)$. We also provide several examples to illustrate how the rotations of hyperrectangles affect $\dim _{\mathrm {H}} W(\mathcal P)$.

Keywords

β-expansionHausdorff dimensionparallelepipedshrinking target problem

MSC classification

Primary: 11K55: Metric theory of other algorithms and expansions; measure and Hausdorff dimension
Secondary: 28A80: Fractals

Information

Type
Original Article
Information
Ergodic Theory and Dynamical Systems , Volume 45 , Issue 6 , June 2025 , pp. 1827 - 1842
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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