Kaisa Matomaki, Maksym Radziwill, and I have uploaded to the arXiv our paper “Correlations of the von Mangoldt and higher divisor functions II. Divisor correlations in short ranges“. This is a sequel of sorts to our previous paper on divisor correlations, though the proof techniques in this paper are rather different. As with the previous paper, our interest is in correlations such as

$\displaystyle \sum_{n \leq X} d_k(n) d_l(n+h) \ \ \ \ \ (1)$

for medium-sized ${h}$ and large ${X}$, where ${k \geq l \geq 1}$ are natural numbers and ${d_k(n) = \sum_{n = m_1 \dots m_k} 1}$ is the ${k^{th}}$ divisor function (actually our methods can also treat a generalisation in which ${k}$ is non-integer, but for simplicity let us stick with the integer case for this discussion). Our methods also allow for one of the divisor function factors to be replaced with a von Mangoldt function, but (in contrast to the previous paper) we cannot treat the case when both factors are von Mangoldt.

As discussed in this previous post, one heuristically expects an asymptotic of the form

$\displaystyle \sum_{n \leq X} d_k(n) d_l(n+h) = P_{k,l,h}( \log X ) X + O( X^{1/2+\varepsilon})$

for any fixed ${\varepsilon>0}$, where ${P_{k,l,h}}$ is a certain explicit (but rather complicated) polynomial of degree ${k+l-1}$. Such asymptotics are known when ${l \leq 2}$, but remain open for ${k \geq l \geq 3}$. In the previous paper, we were able to obtain a weaker bound of the form

$\displaystyle \sum_{n \leq X} d_k(n) d_l(n+h) = P_{k,l,h}( \log X ) X + O_A( X \log^{-A} X)$

for ${1-O_A(\log^{-A} X)}$ of the shifts ${-H \leq h \leq H}$, whenever the shift range ${H}$ lies between ${X^{8/33+\varepsilon}}$ and ${X^{1-\varepsilon}}$. But the methods become increasingly hard to use as ${H}$ gets smaller. In this paper, we use a rather different method to obtain the even weaker bound

$\displaystyle \sum_{n \leq X} d_k(n) d_l(n+h) = (1+o(1)) P_{k,l,h}( \log X ) X$

for ${1-o(1)}$ of the shifts ${-H \leq h \leq H}$, where ${H}$ can now be as short as ${H = \log^{10^4 k \log k} X}$. The constant ${10^4}$ can be improved, but there are serious obstacles to using our method to go below ${\log^{k \log k} X}$ (as the exceptionally large values of ${d_k}$ then begin to dominate). This can be viewed as an analogue to our previous paper on correlations of bounded multiplicative functions on average, in which the functions ${d_k,d_l}$ are now unbounded, and indeed our proof strategy is based in large part on that paper (but with many significant new technical complications).

We now discuss some of the ingredients of the proof. Unsurprisingly, the first step is the circle method, expressing (1) in terms of exponential sums such as

$\displaystyle S(\alpha) := \sum_{n \leq X} d_k(n) e(\alpha).$

Actually, it is convenient to first prune ${d_k}$ slightly by zeroing out this function on “atypical” numbers ${n}$ that have an unusually small or large number of factors in a certain sense, but let us ignore this technicality for this discussion. The contribution of ${S(\alpha)}$ for “major arc” ${\alpha}$ can be treated by standard techniques (and is the source of the main term ${P_{k,l,h}(\log X) X}$; the main difficulty comes from treating the contribution of “minor arc” ${\alpha}$.

In our previous paper on bounded multiplicative functions, we used Plancherel’s theorem to estimate the global ${L^2}$ norm ${\int_{{\bf R}/{\bf Z}} |S(\alpha)|^2\ d\alpha}$, and then also used the Katai-Bourgain-Sarnak-Ziegler orthogonality criterion to control local ${L^2}$ norms ${\int_I |S(\alpha)|^2\ d\alpha}$, where ${I}$ was a minor arc interval of length about ${1/H}$, and these two estimates together were sufficient to get a good bound on correlations by an application of Hölder’s inequality. For ${d_k}$, it is more convenient to use Dirichlet series methods (and Ramaré-type factorisations of such Dirichlet series) to control local ${L^2}$ norms on minor arcs, in the spirit of the proof of the Matomaki-Radziwill theorem; a key point is to develop “log-free” mean value theorems for Dirichlet series associated to functions such as ${d_k}$, so as not to wipe out the (rather small) savings one will get over the trivial bound from this method. On the other hand, the global ${L^2}$ bound will definitely be unusable, because the ${\ell^2}$ sum ${\sum_{n \leq X} d_k(n)^2}$ has too many unwanted factors of ${\log X}$. Fortunately, we can substitute this global ${L^2}$ bound with a “large values” bound that controls expressions such as

$\displaystyle \sum_{i=1}^J \int_{I_i} |S(\alpha)|^2\ d\alpha$

for a moderate number of disjoint intervals ${I_1,\dots,I_J}$, with a bound that is slightly better (for ${J}$ a medium-sized power of ${\log X}$) than what one would have obtained by bounding each integral ${\int_{I_i} |S(\alpha)|^2\ d\alpha}$ separately. (One needs to save more than ${J^{1/2}}$ for the argument to work; we end up saving a factor of about ${J^{3/4}}$.) This large values estimate is probably the most novel contribution of the paper. After taking the Fourier transform, matters basically reduce to getting a good estimate for

$\displaystyle \sum_{i=1}^J (\int_X^{2X} |\sum_{x \leq n \leq x+H} d_k(n) e(\alpha_i n)|^2\ dx)^{1/2},$

where ${\alpha_i}$ is the midpoint of ${I_i}$; thus we need some upper bound on the large local Fourier coefficients of ${d_k}$. These coefficients are difficult to calculate directly, but, in the spirit of a paper of Ben Green and myself, we can try to replace ${d_k}$ by a more tractable and “pseudorandom” majorant ${\tilde d_k}$ for which the local Fourier coefficients are computable (on average). After a standard duality argument, one ends up having to control expressions such as

$\displaystyle |\sum_{x \leq n \leq x+H} \tilde d_k(n) e((\alpha_i -\alpha_{i'}) n)|$

after various averaging in the ${x, i,i'}$ parameters. These local Fourier coefficients of ${\tilde d_k}$ turn out to be small on average unless ${\alpha_i -\alpha_{i'}}$ is “major arc”. One then is left with a mostly combinatorial problem of trying to bound how often this major arc scenario occurs. This is very close to a computation in the previously mentioned paper of Ben and myself; there is a technical wrinkle in that the ${\alpha_i}$ are not as well separated as they were in my paper with Ben, but it turns out that one can modify the arguments in that paper to still obtain a satisfactory estimate in this case (after first grouping nearby frequencies ${\alpha_i}$ together, and modifying the duality argument accordingly).