Multi-parameter Maximal Fourier Restriction

IF 1.2 3区 数学 Q2 MATHEMATICS, APPLIED
Aleksandar Bulj, Vjekoslav Kovač
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引用次数: 0

Abstract

The main result of this note is the strengthening of a quite arbitrary a priori Fourier restriction estimate to a multi-parameter maximal estimate of the same type. This allows us to discuss a certain multi-parameter Lebesgue point property of Fourier transforms, which replaces Euclidean balls by standard ellipsoids or axes-parallel rectangles. Along the lines of the same proof, we also establish a d-parameter Menshov–Paley–Zygmund-type theorem for the Fourier transform on \({\mathbb {R}}^d\). Such a result is interesting for \(d\geqslant 2\) because, in a sharp contrast with the one-dimensional case, the corresponding endpoint \({\text {L}}^2\) estimate (i.e., a Carleson-type theorem) is known to fail since the work of C. Fefferman in 1970. Finally, we show that a Strichartz estimate for a given homogeneous constant-coefficient linear dispersive PDE can sometimes be strengthened to a certain pseudo-differential version.

多参数最大傅立叶限制
本论文的主要成果是将一个相当任意的先验傅立叶限制估计值强化为同一类型的多参数最大估计值。这使我们能够讨论傅里叶变换的某个多参数勒贝格点性质,它用标准椭球或轴平行矩形取代了欧几里得球。按照同样的证明思路,我们还为\({\mathbb {R}}^d\) 上的傅里叶变换建立了一个 d 参数 Menshov-Paley-Zygmund 型定理。对于 \(d\geqslant 2\) 来说,这样的结果是有趣的,因为与一维情况形成鲜明对比的是,自 C. Fefferman 在 1970 年的工作以来,相应的端点 \({\text {L}}^2\) 估计(即 Carleson-type theorem)已知是失败的。最后,我们证明了给定同质常系数线性分散 PDE 的 Strichartz 估计有时可以加强为某个伪差分版本。
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来源期刊
CiteScore
2.10
自引率
16.70%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The Journal of Fourier Analysis and Applications will publish results in Fourier analysis, as well as applicable mathematics having a significant Fourier analytic component. Appropriate manuscripts at the highest research level will be accepted for publication. Because of the extensive, intricate, and fundamental relationship between Fourier analysis and so many other subjects, selected and readable surveys will also be published. These surveys will include historical articles, research tutorials, and expositions of specific topics. TheJournal of Fourier Analysis and Applications will provide a perspective and means for centralizing and disseminating new information from the vantage point of Fourier analysis. The breadth of Fourier analysis and diversity of its applicability require that each paper should contain a clear and motivated introduction, which is accessible to all of our readers. Areas of applications include the following: antenna theory * crystallography * fast algorithms * Gabor theory and applications * image processing * number theory * optics * partial differential equations * prediction theory * radar applications * sampling theory * spectral estimation * speech processing * stochastic processes * time-frequency analysis * time series * tomography * turbulence * uncertainty principles * wavelet theory and applications
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