Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"
Image for the paper "On the random Chowla conjecture"

Random Chowla conjecture

Number theory

The distribution of partial sums of a Steinhaus random multiplicative function, of polynomials in a given form, converges to the standard complex Gaussian.

On the random Chowla conjecture

We show that for a Steinhaus random multiplicative function f:NDf:\mathbb{N}\rightarrow\mathbb{D} and any polynomial P(x)Z[x]P(x)\in\mathbb{Z}[x] of degP2\deg P \geq 2 which is not of the form w(x+c)dw(x+c)^d for some wZ,cQw \in \mathbb{Z}, c \in \mathbb{Q}, we have 1/xnxf(P(n))dCN(0,1)1/\sqrt{x} \cdot \sum_{n\leq x} f(P(n)) \xrightarrow{d} \mathcal{C} \mathcal{N}(0,1), where CN(0,1)\mathcal{C}\mathcal{N}(0,1) is the standard complex Gaussian distribution with mean 0 and variance 1. This confirms a conjecture of Najnudel in a strong form. We further show that there almost surely exist arbitrary large values of x1x \geq 1, such that nxf(P(n))degPx(loglogx)1/2,|\sum_{n\leq x} f(P(n))| \gg_{\deg P} \sqrt{x}(\log \log x)^{1/2}, for any polynomial P(x)Z[x]P(x)\in\mathbb{Z}[x] with degP2\deg P \geq 2, which is not a product of linear factors (over Q\mathbb{Q}). This matches the bound predicted by the law of the iterated logarithm. Both of these results are in contrast with the well-known case of linear phase P(n)=n,P(n) = n, where the partial sums are known to behave in a non-Gaussian fashion and the corresponding sharp fluctuations are speculated to be O(x(loglogx)1/4+ε)O(\sqrt{x}(\log\log x)^{1/4 + \varepsilon}) for any ε>0\varepsilon > 0.