Quantitative bounds for Gowers uniformity of the Mobius and von Mangoldt functions

What's new 2021-07-06

Joni Teräväinen and myself have just uploaded to the arXiv our preprint “Quantitative bounds for Gowers uniformity of the Möbius and von Mangoldt functions“. This paper makes quantitative the Gowers uniformity estimates on the Möbius function ${\mu}$ and the von Mangoldt function ${\Lambda}$ .

To discuss the results we first discuss the situation of the Möbius function, which is technically simpler in some (though not all) ways. We assume familiarity with Gowers norms and standard notations around these norms, such as the averaging notation ${\mathop{\bf E}_{n \in [N]}}$ and the exponential notation ${e(\theta) = e^{2\pi i \theta}}$ . The prime number theorem in qualitative form asserts that

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) = o(1)$

as ${N \rightarrow \infty}$ . With Vinogradov-Korobov error term, the prime number theorem is strengthened to

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) \ll \exp( - c \log^{3/5} N (\log \log N)^{-1/5} );$

we refer to such decay bounds (With ${\exp(-c\log^c N)}$ type factors) as pseudopolynomial decay. Equivalently, we obtain pseudopolynomial decay of Gowers ${U^1}$ seminorm of ${\mu}$ :

$\displaystyle \| \mu \|_{U^1([N])} \ll \exp( - c \log^{3/5} N (\log \log N)^{-1/5} ).$

As is well known, the Riemann hypothesis would be equivalent to an upgrade of this estimate to polynomial decay of the form

$\displaystyle \| \mu \|_{U^1([N])} \ll_\varepsilon N^{-1/2+\varepsilon}$

for any ${\varepsilon>0}$ .

Once one restricts to arithmetic progressions, the situation gets worse: the Siegel-Walfisz theorem gives the bound

$\displaystyle \| \mu 1_{a \hbox{ mod } q}\|_{U^1([N])} \ll_A \log^{-A} N \ \ \ \ \ (1)$

for any residue class ${a \hbox{ mod } q}$ and any ${A>0}$ , but with the catch that the implied constant is ineffective in ${A}$ . This ineffectivity cannot be removed without further progress on the notorious Siegel zero problem.

In 1937, Davenport was able to show the discorrelation estimate

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) e(-\alpha n) \ll_A \log^{-A} N$

for any ${A>0}$ uniformly in ${\alpha \in {\bf R}}$ , which leads (by standard Fourier arguments) to the Fourier uniformity estimate

$\displaystyle \| \mu \|_{U^2([N])} \ll_A \log^{-A} N.$

Again, the implied constant is ineffective. If one insists on effective constants, the best bound currently available is

$\displaystyle \| \mu \|_{U^2([N])} \ll \log^{-c} N \ \ \ \ \ (2)$

for some small effective constant ${c>0}$ .

For the situation with the ${U^3}$ norm the previously known results were much weaker. Ben Green and I showed that

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) \overline{F}(g(n) \Gamma) \ll_{A,F,G/\Gamma} \log^{-A} N \ \ \ \ \ (3)$

uniformly for any ${A>0}$ , any degree two (filtered) nilmanifold ${G/\Gamma}$ , any polynomial sequence ${g: {\bf Z} \rightarrow G}$ , and any Lipschitz function ${F}$ ; again, the implied constants are ineffective. On the other hand, in a separate paper of Ben Green and myself, we established the following inverse theorem: if for instance we knew that

$\displaystyle \| \mu \|_{U^3([N])} \geq \delta$

for some ${0 < \delta < 1/2}$ , then there exists a degree two nilmanifold ${G/\Gamma}$ of dimension ${O( \delta^{-O(1)} )}$ , complexity ${O( \delta^{-O(1)} )}$ , a polynomial sequence ${g: {\bf Z} \rightarrow G}$ , and Lipschitz function ${F}$ of Lipschitz constant ${O(\delta^{-O(1)})}$ such that

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) \overline{F}(g(n) \Gamma) \gg \exp(-\delta^{-O(1)}).$

Putting the two assertions together and comparing all the dependencies on parameters, one can establish the qualitative decay bound

$\displaystyle \| \mu \|_{U^3([N])} = o(1).$

However the decay rate ${o(1)}$ produced by this argument is completely ineffective: obtaining a bound on when this ${o(1)}$ quantity dips below a given threshold ${\delta}$ depends on the implied constant in (3) for some ${G/\Gamma}$ whose dimension depends on ${\delta}$ , and the dependence on ${\delta}$ obtained in this fashion is ineffective in the face of a Siegel zero.

For higher norms ${U^k, k \geq 3}$ , the situation is even worse, because the quantitative inverse theory for these norms is poorer, and indeed it was only with the recent work of Manners that any such bound is available at all (at least for ${k>4}$ ). Basically, Manners establishes if

$\displaystyle \| \mu \|_{U^k([N])} \geq \delta$

then there exists a degree ${k-1}$ nilmanifold ${G/\Gamma}$ of dimension ${O( \delta^{-O(1)} )}$ , complexity ${O( \exp\exp(\delta^{-O(1)}) )}$ , a polynomial sequence ${g: {\bf Z} \rightarrow G}$ , and Lipschitz function ${F}$ of Lipschitz constant ${O(\exp\exp(\delta^{-O(1)}))}$ such that

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) \overline{F}(g(n) \Gamma) \gg \exp\exp(-\delta^{-O(1)}).$

(We allow all implied constants to depend on ${k}$ .) Meanwhile, the bound (3) was extended to arbitrary nilmanifolds by Ben and myself. Again, the two results when concatenated give the qualitative decay

$\displaystyle \| \mu \|_{U^k([N])} = o(1)$

but the decay rate is completely ineffective.

Our first result gives an effective decay bound:

Theorem 1 For any ${k \geq 2}$ , we have ${\| \mu \|_{U^k([N])} \ll (\log\log N)^{-c_k}}$ for some ${c_k>0}$ . The implied constants are effective.

This is off by a logarithm from the best effective bound (2) in the ${k=2}$ case. In the ${k=3}$ case there is some hope to remove this logarithm based on the improved quantitative inverse theory currently available in this case, but there is a technical obstruction to doing so which we will discuss later in this post. For ${k>3}$ the above bound is the best one could hope to achieve purely using the quantitative inverse theory of Manners.

We have analogues of all the above results for the von Mangoldt function ${\Lambda}$ . Here a complication arises that ${\Lambda}$ does not have mean close to zero, and one has to subtract off some suitable approximant ${\Lambda^\sharp}$ to ${\Lambda}$ before one would expect good Gowers norms bounds. For the prime number theorem one can just use the approximant ${1}$ , giving

$\displaystyle \| \Lambda - 1 \|_{U^1([N])} \ll \exp( - c \log^{3/5} N (\log \log N)^{-1/5} )$

but even for the prime number theorem in arithmetic progressions one needs a more accurate approximant. In our paper it is convenient to use the “Cramér approximant”

$\displaystyle \Lambda_{\hbox{Cram\'er}}(n) := \frac{W}{\phi(W)} 1_{(n,W)=1}$

where

$\displaystyle W := \prod_{p<Q} p$

and ${Q}$ is the quasipolynomial quantity

$\displaystyle Q = \exp(\log^{1/10} N). \ \ \ \ \ (4)$

Then one can show from the Siegel-Walfisz theorem and standard bilinear sum methods that

$\displaystyle \mathop{\bf E}_{n \in [N]} (\Lambda - \Lambda_{\hbox{Cram\'er}}(n)) e(-\alpha n) \ll_A \log^{-A} N$

and

$\displaystyle \| \Lambda - \Lambda_{\hbox{Cram\'er}}\|_{U^2([N])} \ll_A \log^{-A} N$

for all ${A>0}$ and ${\alpha \in {\bf R}}$ (with an ineffective dependence on ${A}$ ), again regaining effectivity if ${A}$ is replaced by a sufficiently small constant ${c>0}$ . All the previously stated discorrelation and Gowers uniformity results for ${\mu}$ then have analogues for ${\Lambda}$ , and our main result is similarly analogous:

Theorem 2 For any ${k \geq 2}$ , we have ${\| \Lambda - \Lambda_{\hbox{Cram\'er}} \|_{U^k([N])} \ll (\log\log N)^{-c_k}}$ for some ${c_k>0}$ . The implied constants are effective.

By standard methods, this result also gives quantitative asymptotics for counting solutions to various systems of linear equations in primes, with error terms that gain a factor of ${O((\log\log N)^{-c})}$ with respect to the main term.

We now discuss the methods of proof, focusing first on the case of the Möbius function. Suppose first that there is no “Siegel zero”, by which we mean a quadratic character ${\chi}$ of some conductor ${q \leq Q}$ with a zero ${L(\beta,\chi)}$ with ${1 - \beta \leq \frac{c}{\log Q}}$ for some small absolute constant ${c>0}$ . In this case the Siegel-Walfisz bound (1) improves to a quasipolynomial bound

$\displaystyle \| \mu 1_{a \hbox{ mod } q}\|_{U^1([N])} \ll \exp(-\log^c N). \ \ \ \ \ (5)$

To establish Theorem 1 in this case, it suffices by Manners’ inverse theorem to establish the polylogarithmic bound

$\displaystyle \mathop{\bf E}_{n \in [N]} \mu(n) \overline{F}(g(n) \Gamma) \ll \exp(-\log^c N) \ \ \ \ \ (6)$

for all degree ${k-1}$ nilmanifolds ${G/\Gamma}$ of dimension ${O((\log\log N)^c)}$ and complexity ${O( \exp(\log^c N))}$ , all polynomial sequences ${g}$ , and all Lipschitz functions ${F}$ of norm ${O( \exp(\log^c N))}$ . If the nilmanifold ${G/\Gamma}$ had bounded dimension, then one could repeat the arguments of Ben and myself more or less verbatim to establish this claim from (5), which relied on the quantitative equidistribution theory on nilmanifolds developed in a separate paper of Ben and myself. Unfortunately, in the latter paper the dependence of the quantitative bounds on the dimension ${d}$ was not explicitly given. In an appendix to the current paper, we go through that paper to account for this dependence, showing that all exponents depend at most doubly exponentially in the dimension ${d}$ , which is barely sufficient to handle the dimension of ${O((\log\log N)^c)}$ that arises here.

Now suppose we have a Siegel zero ${L(\beta,\chi)}$ . In this case the bound (5) will not hold in general, and hence also (6) will not hold either. Here, the usual way out (while still maintaining effective estimates) is to approximate ${\mu}$ not by ${0}$ , but rather by a more complicated approximant ${\mu_{\hbox{Siegel}}}$ that takes the Siegel zero into account, and in particular is such that one has the (effective) pseudopolynomial bound

$\displaystyle \| (\mu - \mu_{\hbox{Siegel}}) 1_{a \hbox{ mod } q}\|_{U^1([N])} \ll \exp(-\log^c N) \ \ \ \ \ (7)$

for all residue classes ${a \hbox{ mod } q}$ . The Siegel approximant to ${\mu}$ is actually a little bit complicated, and to our knowledge the first appearance of this sort of approximant only appears as late as this 2010 paper of Germán and Katai. Our version of this approximant is defined as the multiplicative function such that

$\displaystyle \mu_{\hbox{Siegel}}(p^j) = \mu(p^j)$

when ${p < Q}$ , and

$\displaystyle \mu_{\hbox{Siegel}}(n) = \alpha n^{\beta-1} \chi(n)$

when ${n}$ is coprime to all primes ${p<Q}$ , and ${\alpha}$ is a normalising constant given by the formula

$\displaystyle \alpha := \frac{1}{L'(\beta,\chi)} \prod_{p<Q} (1-\frac{1}{p})^{-1} (1 - \frac{\chi(p)}{p^\beta})^{-1}$

(this constant ends up being of size ${O(1)}$ and plays only a minor role in the analysis). This is a rather complicated formula, but it seems to be virtually the only choice of approximant that allows for bounds such as (7) to hold. (This is the one aspect of the problem where the von Mangoldt theory is simpler than the Möbius theory, as in the former one only needs to work with very rough numbers for which one does not need to make any special accommodations for the behavior at small primes when introducing the Siegel correction term.) With this starting point it is then possible to repeat the analysis of my previous papers with Ben and obtain the pseudopolynomial discorrelation bound

$\displaystyle \mathop{\bf E}_{n \in [N]} (\mu - \mu_{\hbox{Siegel}})(n) \overline{F}(g(n) \Gamma) \ll \exp(-\log^c N)$

for ${F(g(n)\Gamma)}$ as before, which when combined with Manners’ inverse theorem gives the doubly logarithmic bound

$\displaystyle \| \mu - \mu_{\hbox{Siegel}} \|_{U^k([N])} \ll (\log\log N)^{-c_k}.$

Meanwhile, a direct sieve-theoretic computation ends up giving the singly logarithmic bound

$\displaystyle \| \mu_{\hbox{Siegel}} \|_{U^k([N])} \ll \log^{-c_k} N$

(indeed, there is a good chance that one could improve the bounds even further, though it is not helpful for this current argument to do so). Theorem 1 then follows from the triangle inequality for the Gowers norm. It is interesting that the Siegel approximant ${\mu_{\hbox{Siegel}}}$ seems to play a rather essential component in the proof, even if it is absent in the final statement. We note that this approximant seems to be a useful tool to explore the “illusory world” of the Siegel zero further; see for instance the recent paper of Chinis for some work in this direction.

For the analogous problem with the von Mangoldt function (assuming a Siegel zero for sake of discussion), the approximant ${\Lambda_{\hbox{Siegel}}}$ is simpler; we ended up using

$\displaystyle \Lambda_{\hbox{Siegel}}(n) = \Lambda_{\hbox{Cram\'er}}(n) (1 - n^{\beta-1} \chi(n))$

which allows one to state the standard prime number theorem in arithmetic progressions with classical error term and Siegel zero term compactly as

$\displaystyle \| (\Lambda - \Lambda_{\hbox{Siegel}}) 1_{a \hbox{ mod } q}\|_{U^1([N])} \ll \exp(-\log^c N).$

Routine modifications of previous arguments also give

$\displaystyle \mathop{\bf E}_{n \in [N]} (\Lambda - \Lambda_{\hbox{Siegel}})(n) \overline{F}(g(n) \Gamma) \ll \exp(-\log^c N) \ \ \ \ \ (8)$

and

$\displaystyle \| \Lambda_{\hbox{Siegel}} \|_{U^k([N])} \ll \log^{-c_k} N.$

The one tricky new step is getting from the discorrelation estimate (8) to the Gowers uniformity estimate

$\displaystyle \| \Lambda - \Lambda_{\hbox{Siegel}} \|_{U^k([N])} \ll (\log\log N)^{-c_k}.$

One cannot directly apply Manners’ inverse theorem here because ${\Lambda}$ and ${\Lambda_{\hbox{Siegel}}}$ are unbounded. There is a standard tool for getting around this issue, now known as the dense model theorem, which is the standard engine powering the transference principle from theorems about bounded functions to theorems about certain types of unbounded functions. However the quantitative versions of the dense model theorem in the literature are expensive and would basically weaken the doubly logarithmic gain here to a triply logarithmic one. Instead, we bypass the dense model theorem and directly transfer the inverse theorem for bounded functions to an inverse theorem for unbounded functions by using the densification approach to transference introduced by Conlon, Fox, and Zhao. This technique turns out to be quantitatively quite efficient (the dependencies of the main parameters in the transference are polynomial in nature), and also has the technical advantage of avoiding the somewhat tricky “correlation condition” present in early transference results which are also not beneficial for quantitative bounds.