$L^2$ Bounds for a maximal directional Hilbert transform

TL;DR

This paper establishes nearly optimal uniform $L^2$ bounds for the maximal directional Hilbert transform in higher dimensions, using polynomial partitioning and orthogonality techniques, with applications to specific direction sets.

Contribution

It provides the first sharp uniform $L^2$ bounds for $H_{oldsymbol{ ext{Omega}}}$ in dimensions 3 and higher, employing polynomial partitioning and an almost-orthogonality principle.

Findings

Established sharp $L^2$ bounds depending only on $N$ for the maximal directional Hilbert transform.

Developed an almost-orthogonality principle applicable to specific direction sets.

Derived stronger $L^2$ estimates for particular direction sets using the new principle.

Abstract

Given any finite direction set $\Omega$ of cardinality $N$ in Euclidean space, we consider the maximal directional Hilbert transform $H_{\Omega}$ associated to this direction set. Our main result provides an essentially sharp uniform bound, depending only on $N$, for the $L^2$ operator norm of $H_{\Omega}$ in dimensions 3 and higher. The main ingredients of the proof consist of polynomial partitioning tools from incidence geometry and an almost-orthogonality principle for $H_{\Omega}$. The latter principle can also be used to analyze special direction sets $\Omega$, and derive sharp $L^2$ estimates for the corresponding operator $H_{\Omega}$ that are typically stronger than the uniform $L^2$ bound mentioned above. A number of such examples are discussed.