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arxiv: 2302.11708 · v3 · pith:NNT4BT7Knew · submitted 2023-02-23 · 🧮 math.CA · math.DS· math.SP

The fractal uncertainty principle via Dolgopyat's method in higher dimensions

classification 🧮 math.CA math.DSmath.SP
keywords deltadimensiondolgopyatfractalmethodprincipleproveuncertainty
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We prove a fractal uncertainty principle with exponent $\frac{d}{2} - \delta + \varepsilon$, $\varepsilon > 0$, for Ahlfors--David regular subsets of $\mathbb R^d$ with dimension $\delta$ which satisfy a suitable "nonorthogonality condition". This generalizes the application of Dolgopyat's method by Dyatlov--Jin (arXiv:1702.03619) to prove the same result in the special case $d = 1$. As a corollary, we get a quantitative spectral gap for the Laplacian on convex cocompact hyperbolic manifolds of arbitrary dimension with Zariski dense fundamental groups.

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  1. Fractal uncertainty principle for random Cantor sets

    math.CA 2024-04 unverdicted novelty 6.0

    Random Cantor sets of dimension d < 2/3 in R satisfy the fractal uncertainty principle with exponent >= 1/2 - 3d/4 - with overwhelming probability, via Fourier decay from concentration of measure.