Escaping Chaos in Random Multiplicative Functions

TL;DR

This paper investigates the behavior of sums of Steinhaus random multiplicative functions over subsets of integers, revealing conditions under which these sums converge to a complex normal distribution and demonstrating the sharpness of the density threshold.

Contribution

It establishes the precise density conditions for sums of random multiplicative functions to converge to a complex normal distribution, confirming the sharpness of the density threshold.

Findings

Sum over set A converges to complex normal if |A|=o(N).

Most sets with density ρ exhibit convergence with a specific normalization.

The density threshold for convergence is shown to be sharp.

Abstract

Let $f(n)$ be a Steinhaus random multiplicative function. Let $A\subset [1, N]$ be a finite set of integers. We show that \[\frac{1}{\sqrt{|A|}} \sum_{n\in A} f(n) \xrightarrow[]{d} \mathcal{CN}(0,1)\] forces that $|A|=o(N)$. We prove that the $o(1)$ density is sharp by showing that for most sets $A$, and thus confirm the existence, with density $\rho$ such that $(1-\rho)^{-1} =o((\log \log N)^{1/2})$, we have \[ \frac{1}{\sqrt{(1-\rho) |A|}} \sum_{n\in A} f(n) \xrightarrow{d} \mathcal{CN}(0,1). \] The extra factor $\sqrt{1-\rho}$ makes a difference as long as the density $\rho>0$.