$\mathbb{R}^d$中的动态自相似性、$L^{q}$维数和弗斯滕伯格切片法

Emilio Corso, Pablo Shmerkin
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引用次数: 0

摘要

我们将第二作者关于实线自相似度量的$L^q$维数定理扩展到了任意维数。即使在一维情况下,我们的方法也提供了新颖、简单的证明。作为结果,我们证明了在温和的分离条件下,$\mathbb{R}^d$中同质自相似度量的$L^q$维数取期望值,并且我们以弗斯滕伯格切片猜想的精神推导出了高阶切片定理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamical self-similarity, $L^{q}$-dimensions and Furstenberg slicing in $\mathbb{R}^d$
We extend a theorem of the second author on the $L^q$-dimensions of dynamically driven self-similar measures from the real line to arbitrary dimension. Our approach provides a novel, simpler proof even in the one-dimensional case. As consequences, we show that, under mild separation conditions, the $L^q$-dimensions of homogeneous self-similar measures in $\mathbb{R}^d$ take the expected values, and we derive higher rank slicing theorems in the spirit of Furstenberg's slicing conjecture.
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