方向平方函数
Directional square functions
AI总结:
本文提出基于方向 Carleson 嵌入与多参数时频分析的平方函数估计框架,得到锥形及 $N$ 方向矩形乘子的定量界,并改进 $N$ 边形 Fourier 限制的最优对数界与加权估计。
摘要:
Fefferman 关于球乘子反例的定量表述,自然地与锥形乘子和方向乘子的平方函数估计相关。本文中,我们基于 Carleson 序列的方向嵌入定理和多参数时频分析技术,为这些平方函数估计建立了一个新框架。作为应用,我们证明了锥形乘子以及适应于沿 $N$ 个方向指向的矩形的乘子的 Rubio de Francia 型平方函数的尖锐或定量化界。这些估计的适当组合给出了 Fourier 限制到 $N$ 边形的一个新的、目前最优的对数界,改进了 A. Cordoba 此前的结果。我们的方向 Carleson 嵌入可推广到加权情形,从而得到方向极大函数和奇异积分此前未知的加权估计。
英文摘要:
Quantitative formulations of Fefferman's counterexample for the ball multiplier are naturally linked to square function estimates for conical and directional multipliers. In this article we develop a novel framework for these square function estimates, based on a directional embedding theorem for Carleson sequences and multi-parameter time-frequency analysis techniques. As applications we prove sharp or quantified bounds for Rubio de Francia type square functions of conical multipliers and of multipliers adapted to rectangles pointing along $N$ directions. A suitable combination of these estimates yields a new and currently best-known logarithmic bound for the Fourier restriction to an $N$-gon, improving on previous results of A. Cordoba. Our directional Carleson embedding extends to the weighted setting, yielding previously unknown weighted estimates for directional maximal functions and singular integrals.
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