The fractal uncertainty principle via Dolgopyat's method in higher dimensions
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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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Fractal uncertainty principle for random Cantor sets
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.
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