Endpoint $\ell^r$ improving estimates for prime averages

IF 0.6 3区 数学 Q3 MATHEMATICS Mathematical Research Letters Pub Date : 2021-01-25 DOI:10.4310/mrl.2022.v29.n6.a6
M. Lacey, H. Mousavi, Yaghoub Rahimi
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引用次数: 2

Abstract

Let $ \Lambda $ denote von Mangoldt's function, and consider the averages \begin{align*} A_N f (x)&=\frac{1}{N}\sum_{1\leq n \leq N}f(x-n)\Lambda(n) . \end{align*} We prove sharp $ \ell ^{p}$-improving for these averages, and sparse bounds for the maximal function. The simplest inequality is that for sets $ F, G\subset [0,N]$ there holds \begin{equation*} N ^{-1} \langle A_N \mathbf 1_{F} , \mathbf 1_{G} \rangle \ll \frac{\lvert F\rvert \cdot \lvert G\rvert} { N ^2 } \Bigl( \operatorname {Log} \frac{\lvert F\rvert \cdot \lvert G\rvert} { N ^2 } \Bigr) ^{t}, \end{equation*} where $ t=2$, or assuming the Generalized Riemann Hypothesis, $ t=1$. The corresponding sparse bound is proved for the maximal function $ \sup_N A_N \mathbf 1_{F}$. The inequalities for $ t=1$ are sharp. The proof depends upon the Circle Method, and an interpolation argument of Bourgain.
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端点$\ well ^r$改善素数平均值的估计
设$ \Lambda $表示von Mangoldt函数,并考虑其平均值\begin{align*} A_N f (x)&=\frac{1}{N}\sum_{1\leq n \leq N}f(x-n)\Lambda(n) . \end{align*}我们证明了这些平均值的显著$ \ell ^{p}$ -改进,以及极大函数的稀疏边界。最简单的不等式是,对于集合$ F, G\subset [0,N]$有\begin{equation*} N ^{-1} \langle A_N \mathbf 1_{F} , \mathbf 1_{G} \rangle \ll \frac{\lvert F\rvert \cdot \lvert G\rvert} { N ^2 } \Bigl( \operatorname {Log} \frac{\lvert F\rvert \cdot \lvert G\rvert} { N ^2 } \Bigr) ^{t}, \end{equation*},其中$ t=2$,或者假设广义黎曼假设,$ t=1$。对极大函数$ \sup_N A_N \mathbf 1_{F}$证明了相应的稀疏界。$ t=1$的不平等非常明显。其证明依据是圆法和布尔甘的插值论证。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
9
审稿时长
6.0 months
期刊介绍: Dedicated to publication of complete and important papers of original research in all areas of mathematics. Expository papers and research announcements of exceptional interest are also occasionally published. High standards are applied in evaluating submissions; the entire editorial board must approve the acceptance of any paper.
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