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arXiv 2602.20086math.NTmath.PR
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随机乘法函数和的大幅波动

Large fluctuations of sums of a random multiplicative function

Fry Building, School of Mathematics, Woodland Road, Bristol BS8 1UG, United Kingdom

Besfort Shala

AI总结:

研究随机乘法函数和的大幅波动,通过量化鞅中心极限定理扩展了中心极限定理框架,证明短区间和多项式值集的和的极限上界。

摘要:

令f为Rademacher或Steinhaus随机乘法函数。对于各种在算术上有趣的子集A⊆[1,N]∩ℕ,使得∑_{n∈A}f(n)的分布近似高斯分布,我们开发了一个通用框架来理解和的大幅波动。这扩展了Soundararajan和Xu的通用中心极限定理框架。当A=(N-H,N]是短区间且H=H(N)可接受时,我们证明几乎必然有lim sup_{N→∞} |∑_{N-H<n≤N}f(n)| / sqrt(H log(N/H)) >0。当A是可接受多项式P∈ℤ[x]的值集时,我们扩展了Klurman、Shkredov和Xu,以及Chinis和作者的工作,证明几乎必然有lim sup_{N→∞} |∑_{n≤N}f(P(n))| / sqrt(N log log N) >0,即使P是有理数域上的线性因子乘积。在此情况下,我们还建立了相应的几乎必然上界,与迭代对数定律相匹配。我们工作中的一个重要成分是通过量化鞅中心极限定理来界定了Kantorovich-Wasserstein距离。

英文摘要:

Let $f$ be a Rademacher or Steinhaus random multiplicative function. For various arithmetically interesting subsets $\mathcal A\subseteq [1, N]\cap\mathbb N$ such that the distribution of $\sum_{n\in \mathcal A} f(n)$ is approximately Gaussian, we develop a general framework to understand the large fluctuations of the sum. This extends the general central limit theorem framework of Soundararajan and Xu. In the case when $\mathcal A = (N-H, N]$ is a short interval with admissible $H=H(N)$, we show that almost surely \begin{equation*} \limsup_{N\to\infty} \frac{\big\lvert\sum_{N-H<n\leq N} f(n)\big\rvert}{\sqrt{H\log \frac{N}H{}}}>0. \end{equation*} When $\mathcal A$ is the set of values of an admissible polynomial $P\in\mathbb Z[x]$, we extend work of Klurman, Shkredov, and Xu, as well as Chinis and the author, showing that almost surely \begin{equation*} \limsup_{N\to\infty} \frac{\big\lvert\sum_{n\leq N} f(P(n))\big\rvert}{\sqrt{N \log\log N}}>0, \end{equation*} even when $P$ is a product of linear factors over $\mathbb Q$. In this case, we also establish the corresponding almost sure upper bound, matching the law of iterated logarithm. An important ingredient in our work is bounding the Kantorovich--Wasserstein distance by means of a quantitative martingale central limit theorem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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