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I’ve just uploaded to the arXiv my paper The Ionescu-Wainger multiplier theorem and the adeles“. This paper revisits a useful multiplier theorem of Ionescu and Wainger on “major arc” Fourier multiplier operators on the integers ${{\bf Z}}$ (or lattices ${{\bf Z}^d}$), and strengthens the bounds while also interpreting it from the viewpoint of the adelic integers ${{\bf A}_{\bf Z}}$ (which were also used in my recent paper with Krause and Mirek).

For simplicity let us just work in one dimension. Any smooth function ${m: {\bf R}/{\bf Z} \rightarrow {\bf C}}$ then defines a discrete Fourier multiplier operator ${T_m: \ell^p({\bf Z}) \rightarrow \ell^p({\bf Z})}$ for any ${1 \leq p \leq \infty}$ by the formula

$\displaystyle {\mathcal F}_{\bf Z} T_m f(\xi) =: m(\xi) {\mathcal F}_{\bf Z} f(\xi)$

where ${{\mathcal F}_{\bf Z} f(\xi) := \sum_{n \in {\bf Z}} f(n) e(n \xi)}$ is the Fourier transform on ${{\bf Z}}$; similarly, any test function ${m: {\bf R} \rightarrow {\bf C}}$ defines a continuous Fourier multiplier operator ${T_m: L^p({\bf R}) \rightarrow L^p({\bf R})}$ by the formula

$\displaystyle {\mathcal F}_{\bf R} T_m f(\xi) := m(\xi) {\mathcal F}_{\bf R} f(\xi)$

where ${{\mathcal F}_{\bf R} f(\xi) := \int_{\bf R} f(x) e(x \xi)\ dx}$. In both cases we refer to ${m}$ as the symbol of the multiplier operator ${T_m}$.

We will be interested in discrete Fourier multiplier operators whose symbols are supported on a finite union of arcs. One way to construct such operators is by “folding” continuous Fourier multiplier operators into various target frequencies. To make this folding operation precise, given any continuous Fourier multiplier operator ${T_m: L^p({\bf R}) \rightarrow L^p({\bf R})}$, and any frequency ${\alpha \in {\bf R}/{\bf Z}}$, we define the discrete Fourier multiplier operator ${T_{m;\alpha}: \ell^p({\bf Z}) \rightarrow \ell^p({\bf Z})}$ for any frequency shift ${\alpha \in {\bf R}/{\bf Z}}$ by the formula

$\displaystyle {\mathcal F}_{\bf Z} T_{m,\alpha} f(\xi) := \sum_{\theta \in {\bf R}: \xi = \alpha + \theta} m(\theta) {\mathcal F}_{\bf Z} f(\xi)$

or equivalently

$\displaystyle T_{m;\alpha} f(n) = \int_{\bf R} m(\theta) {\mathcal F}_{\bf Z} f(\alpha+\theta) e( n(\alpha+\theta) )\ d\theta.$

More generally, given any finite set ${\Sigma \subset {\bf R}/{\bf Z}}$, we can form a multifrequency projection operator ${T_{m;\Sigma}}$ on ${\ell^p({\bf Z})}$ by the formula

$\displaystyle T_{m;\Sigma} := \sum_{\alpha \in \Sigma} T_{m;\alpha}$

thus

$\displaystyle T_{m;\alpha} f(n) = \sum_{\alpha \in \Sigma} \int_{\bf R} m(\theta) {\mathcal F}_{\bf Z} f(\alpha+\theta) e( n(\alpha+\theta) )\ d\theta.$

This construction gives discrete Fourier multiplier operators whose symbol can be localised to a finite union of arcs. For instance, if ${m: {\bf R} \rightarrow {\bf C}}$ is supported on ${[-\varepsilon,\varepsilon]}$, then ${T_{m;\Sigma}}$ is a Fourier multiplier whose symbol is supported on the set ${\bigcup_{\alpha \in \Sigma} \alpha + [-\varepsilon,\varepsilon]}$.

There are a body of results relating the ${\ell^p({\bf Z})}$ theory of discrete Fourier multiplier operators such as ${T_{m;\alpha}}$ or ${T_{m;\Sigma}}$ with the ${L^p({\bf R})}$ theory of their continuous counterparts. For instance we have the basic result of Magyar, Stein, and Wainger:

Proposition 1 (Magyar-Stein-Wainger sampling principle) Let ${1 \leq p \leq \infty}$ and ${\alpha \in {\bf R}/{\bf Z}}$.
• (i) If ${m: {\bf R} \rightarrow {\bf C}}$ is a smooth function supported in ${[-1/2,1/2]}$, then ${\|T_{m;\alpha}\|_{B(\ell^p({\bf Z}))} \lesssim \|T_m\|_{B(L^p({\bf R}))}}$, where ${B(V)}$ denotes the operator norm of an operator ${T: V \rightarrow V}$.
• (ii) More generally, if ${m: {\bf R} \rightarrow {\bf C}}$ is a smooth function supported in ${[-1/2Q,1/2Q]}$ for some natural number ${Q}$, then ${\|T_{m;\alpha + \frac{1}{Q}{\bf Z}/{\bf Z}}\|_{B(\ell^p({\bf Z}))} \lesssim \|T_m\|_{B(L^p({\bf R}))}}$.

When ${p=2}$ the implied constant in these bounds can be set to equal ${1}$. In the paper of Magyar, Stein, and Wainger it was posed as an open problem as to whether this is the case for other ${p}$; in an appendix to this paper I show that the answer is negative if ${p}$ is sufficiently close to ${1}$ or ${\infty}$, but I do not know the full answer to this question.

This proposition allows one to get a good multiplier theory for symbols supported near cyclic groups ${\frac{1}{Q}{\bf Z}/{\bf Z}}$; for instance it shows that a discrete Fourier multiplier with symbol ${\sum_{\alpha \in \frac{1}{Q}{\bf Z}/{\bf Z}} \phi(Q(\xi-\alpha))}$ for a fixed test function ${\phi}$ is bounded on ${\ell^p({\bf Z})}$, uniformly in ${p}$ and ${Q}$. For many applications in discrete harmonic analysis, one would similarly like a good multiplier theory for symbols supported in “major arc” sets such as

$\displaystyle \bigcup_{q=1}^N \bigcup_{\alpha \in \frac{1}{q}{\bf Z}/{\bf Z}} \alpha + [-\varepsilon,\varepsilon] \ \ \ \ \ (1)$

and in particular to get a good Littlewood-Paley theory adapted to major arcs. (This is particularly the case when trying to control “true complexity zero” expressions for which the minor arc contributions can be shown to be negligible; my recent paper with Krause and Mirek is focused on expressions of this type.) At present we do not have a good multiplier theory that is directly adapted to the classical major arc set (1) (though I do not know of rigorous negative results that show that such a theory is not possible); however, Ionescu and Wainger were able to obtain a useful substitute theory in which (1) was replaced by a somewhat larger set that had better multiplier behaviour. Starting with a finite collection ${S}$ of pairwise coprime natural numbers, and a natural number ${k}$, one can form the major arc type set

$\displaystyle \bigcup_{\alpha \in \Sigma_{\leq k}} \alpha + [-\varepsilon,\varepsilon] \ \ \ \ \ (2)$

where ${\Sigma_{\leq k} \subset {\bf R}/{\bf Z}}$ consists of all rational points in the unit circle of the form ${\frac{a}{Q} \mod 1}$ where ${Q}$ is the product of at most ${k}$ elements from ${S}$ and ${a}$ is an integer. For suitable choices of ${S}$ and ${k}$ not too large, one can make this set (2) contain the set (1) while still having a somewhat controlled size (very roughly speaking, one chooses ${S}$ to consist of (small powers of) large primes between ${N^\rho}$ and ${N}$ for some small constant ${\rho>0}$, together with something like the product of all the primes up to ${N^\rho}$ (raised to suitable powers)).

In the regime where ${k}$ is fixed and ${\varepsilon}$ is small, there is a good theory:

Theorem 2 (Ionescu-Wainger theorem, rough version) If ${p}$ is an even integer or the dual of an even integer, and ${m: {\bf R} \rightarrow {\bf C}}$ is supported on ${[-\varepsilon,\varepsilon]}$ for a sufficiently small ${\varepsilon > 0}$, then

$\displaystyle \|T_{m;\Sigma_{\leq k}}\|_{B(\ell^p({\bf Z}))} \lesssim_{p, k} (\log(1+|S|))^{O_k(1)} \|T_m\|_{B(L^p({\bf R}))}.$

There is a more explicit description of how small ${\varepsilon}$ needs to be for this theorem to work (roughly speaking, it is not much more than what is needed for all the arcs ${\alpha + [-\varepsilon,\varepsilon]}$ in (2) to be disjoint), but we will not give it here. The logarithmic loss of ${(\log(1+|S|))^{O_k(1)}}$ was reduced to ${\log(1+|S|)}$ by Mirek. In this paper we refine the bound further to

$\displaystyle \|T_{m;\Sigma_{\leq k}}\|_{B(\ell^p({\bf Z}))} \leq O(r \log(2+kr))^k \|T_m\|_{B(L^p({\bf R}))}. \ \ \ \ \ (3)$

when ${p = 2r}$ or ${p = (2r)'}$ for some integer ${r}$. In particular there is no longer any logarithmic loss in the cardinality of the set ${S}$.

The proof of (3) follows a similar strategy as to previous proofs of Ionescu-Wainger type. By duality we may assume ${p=2r}$. We use the following standard sequence of steps:

• (i) (Denominator orthogonality) First one splits ${T_{m;\Sigma_{\leq k}} f}$ into various pieces depending on the denominator ${Q}$ appearing in the element of ${\Sigma_{\leq k}}$, and exploits “superorthogonality” in ${Q}$ to estimate the ${\ell^p}$ norm by the ${\ell^p}$ norm of an appropriate square function.
• (ii) (Nonconcentration) One expands out the ${p^{th}}$ power of the square function and estimates it by a “nonconcentrated” version in which various factors that arise in the expansion are “disjoint”.
• (iii) (Numerator orthogonality) We now decompose based on the numerators ${a}$ appearing in the relevant elements of ${\Sigma_{\leq k}}$, and exploit some residual orthogonality in this parameter to reduce to estimating a square-function type expression involving sums over various cosets ${\alpha + \frac{1}{Q}{\bf Z}/{\bf Z}}$.
• (iv) (Marcinkiewicz-Zygmund) One uses the Marcinkiewicz-Zygmund theorem relating scalar and vector valued operator norms to eliminate the role of the multiplier ${m}$.
• (v) (Rubio de Francia) Use a reverse square function estimate of Rubio de Francia type to conclude.

The main innovations are that of using the probabilistic decoupling method to remove some logarithmic losses in (i), and recent progress on the Erdos-Rado sunflower conjecture (as discussed in this recent post) to improve the bounds in (ii). For (i), the key point is that one can express a sum such as

$\displaystyle \sum_{A \in \binom{S}{k}} f_A,$

where ${\binom{S}{k}}$ is the set of ${k}$-element subsets of an index set ${S}$, and ${f_A}$ are various complex numbers, as an average

$\displaystyle \sum_{A \in \binom{S}{k}} f_A = \frac{k^k}{k!} {\bf E} \sum_{s_1 \in {\bf S}_1,\dots,s_k \in {\bf S}_k} f_{\{s_1,\dots,s_k\}}$

where ${S = {\bf S}_1 \cup \dots \cup {\bf S}_k}$ is a random partition of ${S}$ into ${k}$ subclasses (chosen uniformly over all such partitions), basically because every ${k}$-element subset ${A}$ of ${S}$ has a probability exactly ${\frac{k!}{k^k}}$ of being completely shattered by such a random partition. This “decouples” the index set ${\binom{S}{k}}$ into a Cartesian product ${{\bf S}_1 \times \dots \times {\bf S}_k}$ which is more convenient for application of the superorthogonality theory. For (ii), the point is to efficiently obtain estimates of the form

$\displaystyle (\sum_{A \in \binom{S}{k}} F_A)^r \lesssim_{k,r} \sum_{A_1,\dots,A_r \in \binom{S}{k} \hbox{ sunflower}} F_{A_1} \dots F_{A_r}$

where ${F_A}$ are various non-negative quantities, and a sunflower is a collection of sets ${A_1,\dots,A_r}$ that consist of a common “core” ${A_0}$ and disjoint “petals” ${A_1 \backslash A_0,\dots,A_r \backslash A_0}$. The other parts of the argument are relatively routine; see for instance this survey of Pierce for a discussion of them in the simple case ${k=1}$.

In this paper we interpret the Ionescu-Wainger multiplier theorem as being essentially a consequence of various quantitative versions of the Shannon sampling theorem. Recall that this theorem asserts that if a (Schwartz) function ${f: {\bf R} \rightarrow {\bf C}}$ has its Fourier transform supported on ${[-1/2,1/2]}$, then ${f}$ can be recovered uniquely from its restriction ${f|_{\bf Z}: {\bf Z} \rightarrow {\bf C}}$. In fact, as can be shown from a little bit of routine Fourier analysis, if we narrow the support of the Fourier transform slightly to ${[-c,c]}$ for some ${0 < c < 1/2}$, then the restriction ${f|_{\bf Z}}$ has the same ${L^p}$ behaviour as the original function, in the sense that

$\displaystyle \| f|_{\bf Z} \|_{\ell^p({\bf Z})} \sim_{c,p} \|f\|_{L^p({\bf R})} \ \ \ \ \ (4)$

for all ${0 < p \leq \infty}$; see Theorem 4.18 of this paper of myself with Krause and Mirek. This is consistent with the uncertainty principle, which suggests that such functions ${f}$ should behave like a constant at scales ${\sim 1/c}$.

The quantitative sampling theorem (4) can be used to give an alternate proof of Proposition 1(i), basically thanks to the identity

$\displaystyle T_{m;0} (f|_{\bf Z}) = (T_m f)_{\bf Z}$

whenever ${f: {\bf R} \rightarrow {\bf C}}$ is Schwartz and has Fourier transform supported in ${[-1/2,1/2]}$, and ${m}$ is also supported on ${[-1/2,1/2]}$; this identity can be easily verified from the Poisson summation formula. A variant of this argument also yields an alternate proof of Proposition 1(ii), where the role of ${{\bf R}}$ is now played by ${{\bf R} \times {\bf Z}/Q{\bf Z}}$, and the standard embedding of ${{\bf Z}}$ into ${{\bf R}}$ is now replaced by the embedding ${\iota_Q: n \mapsto (n, n \hbox{ mod } Q)}$ of ${{\bf Z}}$ into ${{\bf R} \times {\bf Z}/Q{\bf Z}}$; the analogue of (4) is now

$\displaystyle \| f \circ \iota_Q \|_{\ell^p({\bf Z})} \sim_{c,p} \|f\|_{L^p({\bf R} \times {\bf Z}/Q{\bf Z})} \ \ \ \ \ (5)$

whenever ${f: {\bf R} \times {\bf Z}/Q{\bf Z} \rightarrow {\bf C}}$ is Schwartz and has Fourier transform ${{\mathcal F}_{{\bf R} \times {\bf Z}/Q{\bf Z}} f\colon {\bf R} \times \frac{1}{Q}{\bf Z}/{\bf Z} \rightarrow {\bf C}}$ supported in ${[-c/Q,c/Q] \times \frac{1}{Q}{\bf Z}/{\bf Z}}$, and ${{\bf Z}/Q{\bf Z}}$ is endowed with probability Haar measure.

The locally compact abelian groups ${{\bf R}}$ and ${{\bf R} \times {\bf Z}/Q{\bf Z}}$ can all be viewed as projections of the adelic integers ${{\bf A}_{\bf Z} := {\bf R} \times \hat {\bf Z}}$ (the product of the reals and the profinite integers ${\hat {\bf Z}}$). By using the Ionescu-Wainger multiplier theorem, we are able to obtain an adelic version of the quantitative sampling estimate (5), namely

$\displaystyle \| f \circ \iota \|_{\ell^p({\bf Z})} \sim_{c,p} \|f\|_{L^p({\bf A}_{\bf Z})}$

whenever ${1 < p < \infty}$, ${f: {\bf A}_{\bf Z} \rightarrow {\bf C}}$ is Schwartz-Bruhat and has Fourier transform ${{\mathcal F}_{{\bf A}_{\bf Z}} f: {\bf R} \times {\bf Q}/{\bf Z} \rightarrow {\bf C}}$ supported on ${[-\varepsilon,\varepsilon] \times \Sigma_{\leq k}}$ for some sufficiently small ${\varepsilon}$ (the precise bound on ${\varepsilon}$ depends on ${S, p, c}$ in a fashion not detailed here). This allows one obtain an “adelic” extension of the Ionescu-Wainger multiplier theorem, in which the ${\ell^p({\bf Z})}$ operator norm of any discrete multiplier operator whose symbol is supported on major arcs can be shown to be comparable to the ${L^p({\bf A}_{\bf Z})}$ operator norm of an adelic counterpart to that multiplier operator; in principle this reduces “major arc” harmonic analysis on the integers ${{\bf Z}}$ to “low frequency” harmonic analysis on the adelic integers ${{\bf A}_{\bf Z}}$, which is a simpler setting in many ways (mostly because the set of major arcs (2) is now replaced with a product set ${[-\varepsilon,\varepsilon] \times \Sigma_{\leq k}}$).