Introduction
A basic problem in additive combinatorics is to determine which finite patterns must occur in dense subsets of integer lattices. At the qualitative level, the multidimensional Szemerédi theorem of Furstenberg and Katznelson [6 ] gives a general answer: for every finite set F ⊆ Z d F \subseteq\mathbb{Z}^d F ⊆ Z d , every subset of Z d \mathbb{Z}^d Z d of positive upper Banach density contains a homothetic copy x + q F x+qF x + q F , with x ∈ Z d x \in\mathbb{Z}^d x ∈ Z d and q ∈ N q \in\mathbb{N} q ∈ N . The quantitative bounds obtainable from this general theorem are, however, extremely weak; see [8 , 22 ] .
Here we study the corresponding quantitative problem for similarity classes of simplices. Let
Δ = { v 0 , … , v K − 1 } ⊆ Z d \Delta= \{v_0,\ldots,v_{K-1}\} \subseteq\mathbb{Z}^d Δ = { v 0 , … , v K − 1 } ⊆ Z d
be the vertex set of a fixed nondegenerate ( K − 1 ) (K-1) ( K − 1 ) -simplex. We say that Δ ′ = { x 0 , … , x K − 1 } ⊆ Z d \Delta'=\{x_0,\ldots,x_{K-1}\}\subseteq\mathbb{Z}^d Δ ′ = { x 0 , … , x K − 1 } ⊆ Z d is a similar copy of Δ \Delta Δ if
∣ x i − x j ∣ 2 = t ∣ v i − v j ∣ 2 ( 0 ≤ i < j ≤ K − 1 ) (1) \lvert x_i-x_j\rvert^2=t\lvert v_i-v_j\rvert^2 \qquad(0\leq i<j\leq K-1) \tag*{(1)} ∣ x i − x j ∣ 2 = t ∣ v i − v j ∣ 2 ( 0 ≤ i < j ≤ K − 1 ) (1)
for some t > 0 t>0 t > 0 , or equivalently if Δ ′ \Delta' Δ ′ is a translated and rotated image of a dilate of Δ \Delta Δ . Every homothetic copy is similar, so the qualitative existence of such configurations follows from the preceding theorem; our concern is the quantitative dependence on the density.
Our main result is the following.
Theorem 1.1. Let K ≥ 3 K\geq3 K ≥ 3 , and let Δ ⊆ Z d \Delta\subseteq\mathbb{Z}^d Δ ⊆ Z d be the vertex set of a fixed nondegenerate integral ( K − 1 ) (K-1) ( K − 1 ) -simplex. If d ≥ 4 K + 4 d\geq4K+4 d ≥ 4 K + 4 , then there are constants C , c > 0 C,c>0 C , c > 0 , depending only on Δ \Delta Δ and d d d , such that every set A ⊆ [ N ] d A\subseteq[N]^d A ⊆ [ N ] d containing no nontrivial similar copy of Δ \Delta Δ satisfies
∣ A ∣ ≤ C N d exp ( − c log N ) . \lvert A\rvert\leq CN^d\exp\left(-c\sqrt{\log N}\right). ∣ A ∣ ≤ C N d exp ( − c log N ) .
In the common range d ≥ 4 K + 4 d\geq4K+4 d ≥ 4 K + 4 , this improves an earlier result of the second author [17 ] , which states that, when d ≥ 2 K + 3 d\geq2K+3 d ≥ 2 K + 3 , every set A ⊆ [ N ] d A\subseteq[N]^d A ⊆ [ N ] d containing no nontrivial similar copy of Δ \Delta Δ satisfies
∣ A ∣ ≤ C Δ , d N d ( log N ) − 2 / ( 11 K ) . \lvert A\rvert\leq C_{\Delta,d}N^d(\log N)^{-2/(11K)}. ∣ A ∣ ≤ C Δ , d N d ( log N ) − 2/ ( 11 K ) .
To the best of our knowledge, this is the only previous quantitative result formulated for similar copies of an arbitrary nondegenerate integral simplex.
By Chebyshev’s bound, theorem 1.1 immediately gives the following quantitative relative result in the prime lattice; compare this to [3 , 5 , 23 ] . Let P \mathbb{P} P denote the set of primes.
Corollary 1.2. Let K ≥ 3 K\geq3 K ≥ 3 , and let Δ ⊆ Z d \Delta\subseteq\mathbb{Z}^d Δ ⊆ Z d be the vertex set of a fixed nondegenerate integral ( K − 1 ) (K-1) ( K − 1 ) -simplex. If d ≥ 4 K + 4 d\geq4K+4 d ≥ 4 K + 4 , then there are constants C , c > 0 C,c>0 C , c > 0 , depending only on Δ \Delta Δ and d d d , such that every set A ⊆ ( P ∩ [ N ] ) d A\subseteq(\mathbb{P}\cap[N])^d A ⊆ ( P ∩ [ N ] ) d containing no nontrivial similar copy of Δ \Delta Δ satisfies
∣ A ∣ ≤ C ∣ P ∩ [ N ] ∣ d exp ( − c log N ) . \lvert A\rvert\leq C\lvert\mathbb{P}\cap[N]\rvert^d\exp\left(-c\sqrt{\log N}\right). ∣ A ∣ ≤ C ∣ P ∩ [ N ] ∣ d exp ( − c log N ) .
An equivalent form of our main result is that a set A ⊆ [ N ] d A\subseteq[N]^d A ⊆ [ N ] d of density α \alpha α must contain a similar copy once
N ≥ exp ( C Δ , d ( 1 + log ( 1 / α ) ) 2 ) . (2) N\geq\exp\left(C_{\Delta,d}\left(1+\log(1/\alpha)\right)^2\right). \tag*{(2)} N ≥ exp ( C Δ , d ( 1 + log ( 1/ α ) ) 2 ) . (2)
Theorem 1.1 also directly implies the following quantitative result in the Euclidean setting.
Corollary 1.3. Let K ≥ 3 K\geq3 K ≥ 3 , d ≥ 4 K + 4 d\geq4K+4 d ≥ 4 K + 4 , and let Δ ⊆ Z d \Delta\subseteq\mathbb{Z}^d Δ ⊆ Z d be as above. For every measurable E ⊆ [ 0 , 1 ] d E\subseteq[0,1]^d E ⊆ [ 0 , 1 ] d with ∣ E ∣ ≥ α > 0 \lvert E\rvert\geq\alpha>0 ∣ E ∣ ≥ α > 0 , there are x ∈ R d x\in\mathbb{R}^d x ∈ R d , U ∈ O ( d ) U\in O(d) U ∈ O ( d ) , and
λ ≥ exp ( − C Δ , d ( 1 + log ( 1 / α ) ) 2 ) \lambda\geq\exp\left(-C_{\Delta,d}\left(1+\log(1/\alpha)\right)^2\right) λ ≥ exp ( − C Δ , d ( 1 + log ( 1/ α ) ) 2 )
such that x + λ U Δ ⊆ E x+\lambda U\Delta\subseteq E x + λ U Δ ⊆ E .
A short proof, based on averaging Theorem 1.1 over translates of a sufficiently fine lattice, is given in Appendix A. For comparison, a polylogarithmic bound follows from the proof of Bourgain’s simplex theorem in dimensions d ≥ K d \ge K d ≥ K [1 , 16 ] . The cut-operator method also has a direct and simpler implementation in the Euclidean setting, which should permit a lower dimension threshold; we do not pursue this direction here.
Beyond [17 ] , quantitative results in the literature concern special configurations or indirect parametrizations by nonsingular matrix progressions. A notable example is the corners configuration, consisting of axis-parallel isosceles right triangles. Building on ideas originating in the work of Kelley and Meka [11 ] , Jaber, Liu, Lovett, Ostuni and Sawhney [10 ] recently proved that every corner-free set A ⊆ [ N ] 2 A \subseteq[N]^2 A ⊆ [ N ] 2 satisfies
∣ A ∣ ≤ N 2 exp ( − c ( log N ) 1 / 600 ) . |A| \le N^2\exp\left(-c(\log N)^{1/600}\right). ∣ A ∣ ≤ N 2 exp ( − c ( log N ) 1/600 ) .
For an integral planar triangle, complex multiplication gives a parametrization of its similar copies in the form x x x , x + T 1 y x+T_1y x + T 1 y , x + T 2 y x+T_2y x + T 2 y , where T 1 T_1 T 1 , T 2 T_2 T 2 , and T 1 − T 2 T_1-T_2 T 1 − T 2 are nonsingular rational matrices. Thus the problem becomes a matrix-valued version of Roth’s theorem. Quaternionic constructions, and more generally integral orthogonal pairwise anticommuting Hurwitz–Radon matrices [7 ] , similarly give four-term parametrizations of tetrahedral shapes after passage to a suitable auxiliary lattice. Prendiville’s theorem [19 ] then yields a density bound of the form ( log log N ) − c (\log\log N)^{-c} ( log log N ) − c for these parametrizations.
This Hurwitz–Radon construction extends to arbitrary nondegenerate simplices, and more generally to configurations of fixed affine rank, but it has an unavoidable dimensional cost. The sharp Hurwitz–Radon theorem forces a ( K − 1 ) (K-1) ( K − 1 ) -simplex represented in this way to use matrices of order exp ( Ω ( K ) ) \exp(\Omega(K)) exp ( Ω ( K )) ; Geramita and Pullman [7 ] show that the optimal families may be chosen integral. Prendiville’s work also suggests that longer nonsingular matrix progressions should be accessible through higher-order Fourier analysis. Together with the recent quasipolynomial inverse theorem of Leng, Sah and Sawhney [15 , 14 ] , this suggests that one might hope for a density bound of the form
exp ( − ( log log N ) c K ) \exp\left(-(\log\log N)^{c_K}\right) exp ( − ( log log N ) c K )
for each fixed nonsingular matrix progression. Such a result is not presently available: a multidimensional localized density-increment argument would still have to be developed. Even if carried out, this route would give a substantially weaker dependence on N N N than Theorem 1.1 and, through the Hurwitz–Radon parametrization, would require an exponentially large dimension.
Outline of the proof
Setup
Let F 0 , … , F K − 1 : [ N ] d → [ − 1 , 1 ] F_0,\ldots,F_{K-1}:[N]^d\to[-1,1] F 0 , … , F K − 1 : [ N ] d → [ − 1 , 1 ] , extended by zero to Z d \mathbb{Z}^d Z d , and L : = ( K 2 ) L:=\binom{K}{2} L := ( 2 K ) , and let w = 1 [ 1 , 2 ] w=\mathbf{1}_{[1,2]} w = 1 [ 1 , 2 ] . Fix R = ⌈ σ N ⌉ R=\lceil\sigma N\rceil R = ⌈ σ N ⌉ for some sufficiently small constant 0 < σ < 1 0<\sigma<1 0 < σ < 1 depending only on Δ \Delta Δ , assume N N N is sufficiently large depending on Δ \Delta Δ and d d d , and write w R ( n ) = R − 2 w ( n / R 2 ) w_R(n)=R^{-2}w(n/R^2) w R ( n ) = R − 2 w ( n / R 2 ) . We count weighted solutions to equation (1.1) for t = n ∈ N t=n\in\mathbb{N} t = n ∈ N , n ≍ R 2 n\asymp R^2 n ≍ R 2 , via the multilinear expression
Λ N ( F 0 , … , F K − 1 ) = ∑ n w R ( n ) ∑ z 0 , … , z K − 1 ∈ [ N ] d ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( i < j ) ∏ i = 0 K − 1 F i ( z i ) . (3) \Lambda_N(F_0,\ldots,F_{K-1})=
\sum_n w_R(n)
\sum_{\substack{z_0,\ldots,z_{K-1}\in[N]^d\\ |z_i-z_j|^2=n|v_i-v_j|^2\ (i<j)}}
\prod_{i=0}^{K-1}F_i(z_i). \tag*{(3)} Λ N ( F 0 , … , F K − 1 ) = n ∑ w R ( n ) z 0 , … , z K − 1 ∈ [ N ] d ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( i < j ) ∑ i = 0 ∏ K − 1 F i ( z i ) . (3)
Kitaoka’s estimate [13 ] supplies
Λ N ( 1 [ N ] d , … , 1 [ N ] d ) ≳ N d R ( K − 1 ) d − 2 L . (4) \Lambda_N(\mathbf{1}_{[N]^d},\ldots,\mathbf{1}_{[N]^d})\gtrsim N^dR^{(K-1)d-2L}. \tag*{(4)} Λ N ( 1 [ N ] d , … , 1 [ N ] d ) ≳ N d R ( K − 1 ) d − 2 L . (4)
Here ( K − 1 ) d (K-1)d ( K − 1 ) d is the number of relative variables, while 2 L = 2 ( K 2 ) 2L=2\binom{K}{2} 2 L = 2 ( 2 K ) is the contribution of the ( K 2 ) \binom{K}{2} ( 2 K ) quadratic equations.
Suppose that A ⊂ [ N ] d A\subset[N]^d A ⊂ [ N ] d has density α > 0 \alpha>0 α > 0 and contains no nontrivial similar copy of Δ \Delta Δ . Then Λ N ( 1 A , … , 1 A ) = 0 \Lambda_N(1_A,\ldots,1_A)=0 Λ N ( 1 A , … , 1 A ) = 0 . Thus, writing 1 A = α 1 [ N ] d + g 1_A=\alpha1_{[N]^d}+g 1 A = α 1 [ N ] d + g , the pigeonhole principle yields, for some F 0 , … , F K − 1 F_0,\ldots,F_{K-1} F 0 , … , F K − 1 ,
∣ Λ N ( F 0 , … , F K − 1 ) ∣ ≳ α s N d R ( K − 1 ) d − 2 L , (5) \lvert\Lambda_N(F_0,\ldots,F_{K-1})\rvert\gtrsim\alpha^sN^{dR(K-1)d-2L}, \tag*{(5)} ∣ Λ N ( F 0 , … , F K − 1 )∣ ≳ α s N d R ( K − 1 ) d − 2 L , (5)
with exactly s > 0 s>0 s > 0 of the F i F_i F i equal to g g g and the remaining F i F_i F i equal to 1 [ N ] d 1_{[N]^d} 1 [ N ] d . The rest of the proof is to turn this lower bound into a density increment.
As is customary in the circle method, one expresses the quadratic relations via the integrals
1 ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 = ∫ T e ( β i j ∣ z i − z j ∣ 2 ) e ( − n β i j ∣ v i − v j ∣ 2 ) d β i j , e ( t ) : = exp ( 2 π i t ) . 1_{\lvert z_i-z_j\rvert^2=n\lvert v_i-v_j\rvert^2}=\int_{\mathbb T}e(\beta_{ij}\lvert z_i-z_j\rvert^2)e(-n\beta_{ij}\lvert v_i-v_j\rvert^2)\,d\beta_{ij},\qquad e(t):=\exp(2\pi it). 1 ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 = ∫ T e ( β ij ∣ z i − z j ∣ 2 ) e ( − n β ij ∣ v i − v j ∣ 2 ) d β ij , e ( t ) := exp ( 2 π i t ) .
This gives
Λ N ( F 0 , … , F K − 1 ) = ∫ T L w ^ R ( ∑ i < j ∣ v i − v j ∣ 2 β i j ) S β ( F 0 , … , F K − 1 ) d β , ∥ w ^ R ∥ ∞ ≲ 1 , (6) \Lambda_N(F_0,\ldots,F_{K-1})=\int_{\mathbb T^L}\widehat w_R\left(\sum_{i<j}\lvert v_i-v_j\rvert^2\beta_{ij}\right)S_\beta(F_0,\ldots,F_{K-1})\,d\beta,\qquad\lVert\widehat w_R\rVert_\infty\lesssim1, \tag*{(6)} Λ N ( F 0 , … , F K − 1 ) = ∫ T L w R ( i < j ∑ ∣ v i − v j ∣ 2 β ij ) S β ( F 0 , … , F K − 1 ) d β , ∥ w R ∥ ∞ ≲ 1 , (6)
where w ^ R ( t ) : = ∑ n w R ( n ) e ( − n t ) \widehat w_R(t):=\sum_n w_R(n)e(-nt) w R ( t ) := ∑ n w R ( n ) e ( − n t ) and
S β ( F 0 , … , F K − 1 ) = ∑ z 0 , … , z K − 1 ∈ [ N ] d e ( ∑ i < j β i j ∣ z i − z j ∣ 2 ) ∏ i = 0 K − 1 F i ( z i ) . (7) S_\beta(F_0,\ldots,F_{K-1})=\sum_{z_0,\ldots,z_{K-1}\in[N]^d}e\left(\sum_{i<j}\beta_{ij}\lvert z_i-z_j\rvert^2\right)\prod_{i=0}^{K-1}F_i(z_i). \tag*{(7)} S β ( F 0 , … , F K − 1 ) = z 0 , … , z K − 1 ∈ [ N ] d ∑ e ( i < j ∑ β ij ∣ z i − z j ∣ 2 ) i = 0 ∏ K − 1 F i ( z i ) . (7)
The cut-operator reduction
Write E E E for the edge set of the complete graph on K K K vertices, and note that L = ∣ E ∣ = ( K 2 ) L=\lvert E\rvert=\binom{K}{2} L = ∣ E ∣ = ( 2 K ) . We use the convention β j i = β i j \beta_{ji}=\beta_{ij} β j i = β ij for i < j i<j i < j .
Fix a balanced cut S ∣ T S\mid T S ∣ T , that is, a partition of the vertex set into two classes whose sizes differ by at most one. The phase Q β ( z ) Q_\beta(z) Q β ( z ) in (7) decomposes as
Q β ( z ) = Q β S ( z S ) + Q β T ( z T ) − 2 ∑ i ∈ S , j ∈ T β i j z i ⋅ z j , Q_\beta(z)=Q_\beta^S(z_S)+Q_\beta^T(z_T)-2\sum_{i\in S,j\in T}\beta_{ij}z_i\cdot z_j, Q β ( z ) = Q β S ( z S ) + Q β T ( z T ) − 2 i ∈ S , j ∈ T ∑ β ij z i ⋅ z j ,
where z S = ( z i ) i ∈ S z_S=(z_i)_{i\in S} z S = ( z i ) i ∈ S and z T = ( z j ) j ∈ T z_T=(z_j)_{j\in T} z T = ( z j ) j ∈ T . The first two terms depend on only one side of the cut. For fixed Θ ∈ T ∣ S ∣ ∣ T ∣ \Theta\in\mathbb T^{\lvert S\rvert\lvert T\rvert} Θ ∈ T ∣ S ∣ ∣ T ∣ , the crossing term naturally leads to the operator
K R , d S , T ( Θ ) : ℓ 2 ( ( [ R ] d ) T ) → ℓ 2 ( ( [ R ] d ) S ) \mathcal K_{R,d}^{S,T}(\Theta):\ell^2(([R]^d)^T)\to\ell^2(([R]^d)^S) K R , d S , T ( Θ ) : ℓ 2 (([ R ] d ) T ) → ℓ 2 (([ R ] d ) S )
defined by
( K R , d S , T ( Θ ) h ) ( z S ) : = ∑ z T ∈ ( [ R ] d ) T e ( ∑ i ∈ S , j ∈ T Θ i j z i ⋅ z j ) h ( z T ) . (\mathcal K_{R,d}^{S,T}(\Theta)h)(z_S):=\sum_{z_T\in([R]^d)^T}e\left(\sum_{i\in S,j\in T}\Theta_{ij}z_i\cdot z_j\right)h(z_T). ( K R , d S , T ( Θ ) h ) ( z S ) := z T ∈ ([ R ] d ) T ∑ e i ∈ S , j ∈ T ∑ Θ ij z i ⋅ z j h ( z T ) .
We call K R , d S , T ( Θ ) \mathcal K_{R,d}^{S,T}(\Theta) K R , d S , T ( Θ ) a cut operator. Because the dot product separates coordinatewise, K R , d S , T ( Θ ) \mathcal K_{R,d}^{S,T}(\Theta) K R , d S , T ( Θ ) is the d d d -fold tensor product of the operator K R S , T ( Θ ) : = K R , 1 S , T ( Θ ) \mathcal K_R^{S,T}(\Theta):=\mathcal K_{R,1}^{S,T}(\Theta) K R S , T ( Θ ) := K R , 1 S , T ( Θ ) . For ease of notation, we work with the normalized operator norm
m R S , T ( Θ ) : = R − K / 2 ∥ K R S , T ( Θ ) ∥ 2 → 2 , ( 0 ≤ m R S , T ( Θ ) ≤ 1 ) . (8) m_R^{S,T}(\Theta):=R^{-K/2}\lVert\mathcal K_R^{S,T}(\Theta)\rVert_{2\to2},\qquad(0\le m_R^{S,T}(\Theta)\le1). \tag*{(8)} m R S , T ( Θ ) := R − K /2 ∥ K R S , T ( Θ ) ∥ 2 → 2 , ( 0 ≤ m R S , T ( Θ ) ≤ 1 ) . (8)
Let β S , T = ( β i j ) i ∈ S , j ∈ T \beta_{S,T}=(\beta_{ij})_{i\in S,j\in T} β S , T = ( β ij ) i ∈ S , j ∈ T denote the family of crossing frequencies of the cut S ∣ T S\mid T S ∣ T . A key observation is to write
S β ( F 0 , … , F K − 1 ) = ⟨ U S K N , d S , T ( − 2 β S , T ) U T H T , H S ⟩ S_\beta(F_0,\ldots,F_{K-1})=\left\langle U_S\mathcal K_{N,d}^{S,T}(-2\beta_{S,T})U_TH_T,H_S\right\rangle S β ( F 0 , … , F K − 1 ) = ⟨ U S K N , d S , T ( − 2 β S , T ) U T H T , H S ⟩
where
H S ( z S ) : = ∏ i ∈ S F i ( z i ) , H T ( z T ) : = ∏ j ∈ T F j ( z j ) . H_S(z_S) := \prod_{i\in S} F_i(z_i), \qquad H_T(z_T) := \prod_{j\in T} F_j(z_j). H S ( z S ) := i ∈ S ∏ F i ( z i ) , H T ( z T ) := j ∈ T ∏ F j ( z j ) .
and U S , U T U_S,U_T U S , U T are the unitary multiplication operators
( U S h ) ( z S ) : = e ( Q β S ( z S ) ) h ( z S ) , ( U T h ) ( z T ) : = e ( Q β T ( z T ) ) h ( z T ) . (U_S h)(z_S) := e(Q^S_\beta(z_S))h(z_S), \qquad(U_T h)(z_T) := e(Q^T_\beta(z_T))h(z_T). ( U S h ) ( z S ) := e ( Q β S ( z S )) h ( z S ) , ( U T h ) ( z T ) := e ( Q β T ( z T )) h ( z T ) .
Since ∥ H S ∥ 2 = ∏ i ∈ S ∥ F i ∥ 2 \lVert H_S\rVert_2 = \prod_{i\in S}\lVert F_i\rVert_2 ∥ H S ∥ 2 = ∏ i ∈ S ∥ F i ∥ 2 and ∥ H T ∥ 2 = ∏ j ∈ T ∥ F j ∥ 2 \lVert H_T\rVert_2 = \prod_{j\in T}\lVert F_j\rVert_2 ∥ H T ∥ 2 = ∏ j ∈ T ∥ F j ∥ 2 , the Cauchy–Schwarz inequality, unitarity, and the multiplicativity of the ℓ 2 → ℓ 2 \ell^2 \to\ell^2 ℓ 2 → ℓ 2 operator norm with respect to tensor products imply that
∣ S β ( F 0 , … , F K − 1 ) ∣ ≤ N K d / 2 m N S , T ( − 2 β S , T ) d ∏ i = 0 K − 1 ∥ F i ∥ 2 . (9) \lvert\mathcal{S}_\beta(F_0,\ldots,F_{K-1})\rvert\le N^{Kd/2}m_N^{S,T}(-2\beta_{S,T})^d\prod_{i=0}^{K-1}\lVert F_i\rVert_2. \tag*{(9)} ∣ S β ( F 0 , … , F K − 1 )∣ ≤ N K d /2 m N S , T ( − 2 β S , T ) d i = 0 ∏ K − 1 ∥ F i ∥ 2 . (9)
Let B K \mathcal{B}_K B K be the set of balanced cuts; we introduce the notation
w K : = L ∣ S ∣ ∣ T ∣ ∣ B K ∣ , τ : = d ∣ S ∣ ∣ T ∣ 2 L . w_K := \frac{L}{\lvert S\rvert\lvert T\rvert\lvert\mathcal{B}_K\rvert}, \qquad\tau:= \frac{d\lvert S\rvert\lvert T\rvert}{2L}. w K := ∣ S ∣ ∣ T ∣ ∣ B K ∣ L , τ := 2 L d ∣ S ∣ ∣ T ∣ .
By symmetry it is easy to see that
w K # { S ∣ T ∈ B K : e crosses S ∣ T } = 1 ( e ∈ E ) . (10) w_K\#\{S\mid T\in\mathcal{B}_K:e\text{ crosses }S\mid T\}=1 \qquad(e\in\mathcal{E}). \tag*{(10)} w K # { S ∣ T ∈ B K : e crosses S ∣ T } = 1 ( e ∈ E ) . (10)
Since 2 τ w K = d / ∣ B K ∣ 2\tau w_K=d/\lvert\mathcal{B}_K\rvert 2 τ w K = d / ∣ B K ∣ , taking the geometric mean of (9) over S ∣ T ∈ B K S\mid T\in\mathcal{B}_K S ∣ T ∈ B K yields
∣ S β ( F 0 , … , F K − 1 ) ∣ ≤ N K d / 2 ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K ∏ i = 0 K − 1 ∥ F i ∥ 2 . (11) \lvert\mathcal{S}_\beta(F_0,\ldots,F_{K-1})\rvert\le N^{Kd/2}\prod_{S\mid T\in\mathcal{B}_K}m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K}\prod_{i=0}^{K-1}\lVert F_i\rVert_2. \tag*{(11)} ∣ S β ( F 0 , … , F K − 1 )∣ ≤ N K d /2 S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K i = 0 ∏ K − 1 ∥ F i ∥ 2 . (11)
The problem of estimating the exponential sum S β \mathcal{S}_\beta S β has thus been reduced to understanding the normalized one-dimensional cut-operator norms.
The mean-value estimate
The first principal estimate is a high-moment bound for the cut operator norm.
Lemma 2.1. Let S ∣ T S\mid T S ∣ T be a balanced cut. For every positive integer R R R ,
∫ T ∣ S ∣ ∣ T ∣ m R S , T ( Θ ) 2 K + 4 d Θ ≪ K R − 2 ∣ S ∣ ∣ T ∣ . (12) \int_{\mathbb{T}^{\lvert S\rvert\lvert T\rvert}}m_R^{S,T}(\Theta)^{2K+4}\,d\Theta\ll_K R^{-2\lvert S\rvert\lvert T\rvert}. \tag*{(12)} ∫ T ∣ S ∣ ∣ T ∣ m R S , T ( Θ ) 2 K + 4 d Θ ≪ K R − 2 ∣ S ∣ ∣ T ∣ . (12)
The power R − 2 ∣ S ∣ ∣ T ∣ R^{-2\lvert S\rvert\lvert T\rvert} R − 2 ∣ S ∣ ∣ T ∣ is the natural saving associated with the ∣ S ∣ ∣ T ∣ \lvert S\rvert\lvert T\rvert ∣ S ∣ ∣ T ∣ crossing frequencies. To prove the lemma, we observe first that
∥ K R S , T ( Θ ) ∥ 2 → 2 2 K + 4 = ∥ ( K R S , T ( Θ ) K R S , T ( Θ ) ∗ ) K + 2 ∥ 2 → 2 \lVert\mathcal{K}_R^{S,T}(\Theta)\rVert_{2\to2}^{2K+4}
=
\lVert(\mathcal{K}_R^{S,T}(\Theta)\mathcal{K}_R^{S,T}(\Theta)^*)^{K+2}\rVert_{2\to2} ∥ K R S , T ( Θ ) ∥ 2 → 2 2 K + 4 = ∥( K R S , T ( Θ ) K R S , T ( Θ ) ∗ ) K + 2 ∥ 2 → 2
and that the latter norm is bounded by the trace of ( K R S , T ( Θ ) K R S , T ( Θ ) ∗ ) K + 2 (\mathcal{K}_R^{S,T}(\Theta)\mathcal{K}_R^{S,T}(\Theta)^*)^{K+2} ( K R S , T ( Θ ) K R S , T ( Θ ) ∗ ) K + 2 . Expanding the trace and integrating in Θ \Theta Θ reduces the problem, up to at most R K R^K R K choices of base points, to counting bounded integer matrices
U ∈ Z ∣ S ∣ × ( K + 1 ) , V ∈ Z ∣ T ∣ × ( K + 1 ) , U V T = 0. (13) U\in\mathbb{Z}^{\lvert S\rvert\times(K+1)},\qquad V\in\mathbb{Z}^{\lvert T\rvert\times(K+1)},\qquad UV^{\mathsf T}=0. \tag*{(13)} U ∈ Z ∣ S ∣ × ( K + 1 ) , V ∈ Z ∣ T ∣ × ( K + 1 ) , U V T = 0. (13)
We follow a geometric route exploiting the special structure of this matrix equation. In particular, after treating U = 0 U=0 U = 0 and V = 0 V=0 V = 0 separately, the saturated row lattices Γ U \Gamma_U Γ U and Γ V \Gamma_V Γ V given by U U U and $V determine the primitive 2-step lattice flag Γ U ⊆ Γ V ⊥ ⊆ Z K + 1 \Gamma_U \subseteq\Gamma_V^{\perp} \subseteq\mathbb{Z}^{K+1} Γ U ⊆ Γ V ⊥ ⊆ Z K + 1 , where Γ V ⊥ \Gamma_V^{\perp} Γ V ⊥ is the integral orthogonal complement of Γ V \Gamma_V Γ V . Thus we reduce the count of solutions to the matrix equation to counting primitive 2-step lattice flags subject to certain constraints. Indeed, organizing the count by its ranks and covolumes, and combining geometry of numbers with Kim’s mean-value formula for primitive lattice flags [12 ] , gives the upper bound
# { ( U , V ) : U V T = 0 , ∥ U ∥ ∞ , ∥ V ∥ ∞ ≪ R } ≪ K R K ( K + 1 ) − 2 ∣ S ∣ ∣ T ∣ . \#\{(U,V):UV^{\mathsf T}=0,\ \lVert U\rVert_\infty,\lVert V\rVert_\infty\ll R\}\ll_K R^{K(K+1)-2|S||T|}. # {( U , V ) : U V T = 0 , ∥ U ∥ ∞ , ∥ V ∥ ∞ ≪ R } ≪ K R K ( K + 1 ) − 2∣ S ∣∣ T ∣ .
Combining this with the R K R^K R K choices of base points gives precisely the bound required in Lemma (3) after normalization by R − K ( K + 2 ) R^{-K(K+2)} R − K ( K + 2 ) .
Finner’s fractional form of Hölder’s inequality [4 ] turns the one-cut estimates into the global estimate used in both the major and minor arc estimates.
Corollary 2.2. If τ ≥ K + 2 \tau\ge K+2 τ ≥ K + 2 , then
∫ T L ∏ S ∣ T ∈ B K m R S , T ( − 2 θ S , T ) 2 τ w K d θ ≪ K , d R − 2 L . (14) \int_{\mathbb{T}^L}\prod_{S|T\in\mathcal{B}_K}m_R^{S,T}(-2\theta_{S,T})^{2\tau w_K}\,d\theta\ll_{K,d}R^{-2L}. \tag*{(14)} ∫ T L S ∣ T ∈ B K ∏ m R S , T ( − 2 θ S , T ) 2 τ w K d θ ≪ K , d R − 2 L . (14)
Indeed, the covering condition in Finner’s inequality is exactly (10) . Since m R S , T ≤ 1 m_R^{S,T}\le1 m R S , T ≤ 1 and 2 τ ≥ 2 K + 4 2\tau\ge2K+4 2 τ ≥ 2 K + 4 , Lemma (3) gives
∫ T L ∏ S ∣ T ∈ B K m R S , T ( − 2 θ S , T ) 2 τ w K d θ ≤ ∏ S ∣ T ∈ B K ( ∫ T ∣ S ∣ ∣ T ∣ m R S , T ( Θ ) 2 τ d Θ ) w K ≪ ∏ S ∣ T ∈ B K R − 2 ∣ S ∣ ∣ T ∣ w K = R − 2 L . \begin{aligned}
\int_{\mathbb{T}^L}\prod_{S|T\in\mathcal{B}_K}m_R^{S,T}(-2\theta_{S,T})^{2\tau w_K}\,d\theta
\le\prod_{S|T\in\mathcal{B}_K}\left(\int_{\mathbb{T}^{|S||T|}}m_R^{S,T}(\Theta)^{2\tau}\,d\Theta\right)^{w_K} \\
&\ll\prod_{S|T\in\mathcal{B}_K}R^{-2|S||T|w_K}=R^{-2L}.
\end{aligned} ∫ T L S ∣ T ∈ B K ∏ m R S , T ( − 2 θ S , T ) 2 τ w K d θ ≤ S ∣ T ∈ B K ∏ ( ∫ T ∣ S ∣∣ T ∣ m R S , T ( Θ ) 2 τ d Θ ) w K ≪ S ∣ T ∈ B K ∏ R − 2∣ S ∣∣ T ∣ w K = R − 2 L .
For K = 4 K=4 K = 4 , the mechanism is especially transparent. There are six edge frequencies and three balanced 2 ∣ 2 2\mid2 2 ∣ 2 cuts,
01 ∣ 23 , 02 ∣ 13 , 03 ∣ 12. 01\mid23,\qquad02\mid13,\qquad03\mid12. 01 ∣ 23 , 02 ∣ 13 , 03 ∣ 12.
Each cut sees four edges, every edge crosses exactly two cuts, and w 4 = 1 / 2 w_4=1/2 w 4 = 1/2 . Lemma (3) is a twelfth-moment estimate, and Finner’s inequality gives the bound
( R − 8 ) 1 / 2 ( R − 8 ) 1 / 2 ( R − 8 ) 1 / 2 = R − 12 = R − 2 ( 4 ) . (R^{-8})^{1/2}(R^{-8})^{1/2}(R^{-8})^{1/2}=R^{-12}=R^{-2(4)}. ( R − 8 ) 1/2 ( R − 8 ) 1/2 ( R − 8 ) 1/2 = R − 12 = R − 2 ( 4 ) .
It is natural to ask whether the lattice-flag count can be replaced by a general circle-method or modular-form estimate. Because (13) is a singular system with low-rank pencils, direct application of the general circle-method estimates in [20 , 9 ] would require taking a trace moment of order proportional to K 2 K^2 K 2 and would ultimately lead to a dimension hypothesis d ≫ K 2 d\gg K^2 d ≫ K 2 . Kitaoka’s theory of Siegel modular forms treats related positive-definite representation problems [13 ] , but we are unaware of a modular-form estimate that directly supplies the required upper bound for this singular, indefinite matrix equation. The lattice-flag argument exploits its bilinear structure and is what allows the dimension threshold to remain linear in K K K .
The major and minor arc estimates
The major arcs.
Fix a suitable parameter 2 ≤ Q ≤ R 2\le Q\le R 2 ≤ Q ≤ R . On a major arc, after passing to a common denominator, write β = a / q + θ \beta=a/q+\theta β = a / q + θ , where q ≤ 2 Q L q\le2Q^L q ≤ 2 Q L , ( a , q ) = 1 (a,q)=1 ( a , q ) = 1 , and ∥ θ ∥ ∞ ≤ Q / R 2 \lVert\theta\rVert_\infty\le Q/R^2 ∥ θ ∥ ∞ ≤ Q / R 2 . We may approximate S a / q + θ S_{a/q+\theta} S a / q + θ , at the cost of a negligible error, by a major arc form, consisting of a complete-sum component and an archimedean component:
M q , a , θ ( G 0 , … , G K − 1 ) = E r m o d q ∑ u ∈ ( Z d ) K e q ( Q a ( r ) ) e ( Q θ ( u ) ) ∏ i = 0 K − 1 G i ( u i , r i ) . \mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1})=\mathbb{E}_{r\ \mathrm{mod}\ q}\sum_{u\in(\mathbb{Z}^d)^K}e_q(Q_a(r))e(Q_\theta(u))\prod_{i=0}^{K-1}G_i(u_i,r_i). M q , a , θ ( G 0 , … , G K − 1 ) = E r mod q u ∈ ( Z d ) K ∑ e q ( Q a ( r )) e ( Q θ ( u )) i = 0 ∏ K − 1 G i ( u i , r i ) .
where each G i G_i G i is obtained from the convolution of F i F_i F i with the normalized counting measure μ q \mu_q μ q on a q q q -spaced box, defined below. More precisely, G i ( u , r ) = ( μ q ∗ F i ) ( y ) G_i(u,r)=(\mu_q*F_i)(y) G i ( u , r ) = ( μ q ∗ F i ) ( y ) , where y ≡ r ( m o d q ) y\equiv r\pmod q y ≡ r ( mod q ) and u − y ∈ { 0 , … , q − 1 } d u-y\in\{0,\ldots,q-1\}^d u − y ∈ { 0 , … , q − 1 } d .
Define an operator
K q , a , d S , T : ℓ 2 ( ( Z / q Z ) d T ) ⟶ ℓ 2 ( ( Z / q Z ) d S ) \mathcal{K}^{S,T}_{q,a,d}:\ell^2((\mathbb{Z}/q\mathbb{Z})^{dT})\longrightarrow\ell^2((\mathbb{Z}/q\mathbb{Z})^{dS}) K q , a , d S , T : ℓ 2 (( Z / q Z ) d T ) ⟶ ℓ 2 (( Z / q Z ) d S )
with normalized counting measure on both spaces, given by
( K q , a , d S , T H ) ( r S ) : = E r T m o d q e q ( − 2 ∑ i ∈ S , j ∈ T a i j r i ⋅ r j ) H ( r T ) . (\mathcal{K}^{S,T}_{q,a,d}H)(r_S):=\mathbb{E}_{r_T\ \mathrm{mod}\ q}e_q\left(-2\sum_{\substack{i\in S,j\in T}}a_{ij}r_i\cdot r_j\right)H(r_T). ( K q , a , d S , T H ) ( r S ) := E r T mod q e q − 2 i ∈ S , j ∈ T ∑ a ij r i ⋅ r j H ( r T ) .
By a similar cut reduction, for each cut S ∣ T S\mid T S ∣ T , M q , a , θ \mathcal{M}_{q,a,\theta} M q , a , θ is controlled by K q , a , d S , T ⊗ K P , d S , T ( − 2 θ S , T ) \mathcal{K}^{S,T}_{q,a,d}\otimes\mathcal{K}^{S,T}_{P,d}(-2\theta_{S,T}) K q , a , d S , T ⊗ K P , d S , T ( − 2 θ S , T ) , where P P P is a suitably chosen parameter proportional to N N N . In this way, M q , a , θ \mathcal{M}_{q,a,\theta} M q , a , θ itself does not factor, but the cut operator is a tensor product and thus its operator norm does factor:
∥ K q , a , d S , T ⊗ K P , d S , T ( − 2 θ S , T ) ∥ 2 → 2 = ∥ K q , a , d S , T ∥ 2 → 2 ∥ K P , d S , T ( − 2 θ S , T ) ∥ 2 → 2 . \left\|\mathcal{K}^{S,T}_{q,a,d}\otimes\mathcal{K}^{S,T}_{P,d}(-2\theta_{S,T})\right\|_{2\to2}
=
\left\|\mathcal{K}^{S,T}_{q,a,d}\right\|_{2\to2}
\left\|\mathcal{K}^{S,T}_{P,d}(-2\theta_{S,T})\right\|_{2\to2}. K q , a , d S , T ⊗ K P , d S , T ( − 2 θ S , T ) 2 → 2 = K q , a , d S , T 2 → 2 K P , d S , T ( − 2 θ S , T ) 2 → 2 .
Hence, the major arc analysis reduces to understanding the operator norms of K q , a , d S , T \mathcal{K}^{S,T}_{q,a,d} K q , a , d S , T and K P , d S , T ( − 2 θ S , T ) \mathcal{K}^{S,T}_{P,d}(-2\theta_{S,T}) K P , d S , T ( − 2 θ S , T ) . An elementary orthogonality argument gives
∥ K q , a , d S , T ∥ 2 → 2 = ∣ im ( 2 a S ∣ T ) ∣ − d / 2 , (15) \left\|\mathcal{K}^{S,T}_{q,a,d}\right\|_{2\to2}=\left|\operatorname{im}(2a_{S\mid T})\right|^{-d/2}, \tag*{(15)} K q , a , d S , T 2 → 2 = im ( 2 a S ∣ T ) − d /2 , (15)
where the matrix 2 a S ∣ T = ( 2 a i j ) i ∈ S , j ∈ T 2a_{S\mid T}=(2a_{ij})_{i\in S,j\in T} 2 a S ∣ T = ( 2 a ij ) i ∈ S , j ∈ T is regarded as a homomorphism ( Z / q Z ) T → ( Z / q Z ) S (\mathbb{Z}/q\mathbb{Z})^T\to(\mathbb{Z}/q\mathbb{Z})^S ( Z / q Z ) T → ( Z / q Z ) S . Corollary 2.2 treats K P , d S , T \mathcal{K}^{S,T}_{P,d} K P , d S , T , and we treat K q , a , d S , T \mathcal{K}^{S,T}_{q,a,d} K q , a , d S , T via row and column operations and an elementary counting argument.
The minor arcs.
The minor arcs require an additional pointwise estimate. For ξ ∈ T \xi\in\mathbb{T} ξ ∈ T , set
K R ( ξ ) = ( e ( ξ r s ) ) r , s ∈ [ R ] , m R ( ξ ) = R − 1 ∥ K R ( ξ ) ∥ 2 → 2 . K_R(\xi)=\left(e(\xi r s)\right)_{r,s\in[R]},\qquad m_R(\xi)=R^{-1}\left\|K_R(\xi)\right\|_{2\to2}. K R ( ξ ) = ( e ( ξ r s ) ) r , s ∈ [ R ] , m R ( ξ ) = R − 1 ∥ K R ( ξ ) ∥ 2 → 2 .
After isolating the variables attached to a crossing edge e = ( i , j ) e=(i,j) e = ( i , j ) , a simple application of Cauchy–Schwarz in the remaining variables gives
m R S , T ( Θ ) ≤ m R ( Θ i j ) ( i ∈ S , j ∈ T ) . (16) m_R^{S,T}(\Theta)\leq m_R(\Theta_{ij})\qquad(i\in S,\ j\in T). \tag*{(16)} m R S , T ( Θ ) ≤ m R ( Θ ij ) ( i ∈ S , j ∈ T ) . (16)
Lemma 2.3. Let 2 ≤ Q ≤ R 2\leq Q\leq R 2 ≤ Q ≤ R . Suppose that ξ ∈ T \xi\in\mathbb{T} ξ ∈ T admits no coprime rational approximation a / q a/q a / q satisfying
1 ≤ q ≤ Q , ∥ ξ − a / q ∥ T ≤ Q q R 2 . 1\leq q\leq Q,\qquad\left\|\xi-a/q\right\|_{\mathbb{T}}\leq\frac{Q}{qR^2}. 1 ≤ q ≤ Q , ∥ ξ − a / q ∥ T ≤ q R 2 Q .
Then
m R ( ξ ) 2 ≪ Q − 1 . (17) m_R(\xi)^2\ll Q^{-1}. \tag*{(17)} m R ( ξ ) 2 ≪ Q − 1 . (17)
Lemma 2.3 is a Weyl-type estimate; we prove it using the large sieve inequality. Let m Q \mathbf{m}_Q m Q be the set of β ∈ T L \beta\in\mathbb{T}^{L} β ∈ T L for which at least one edge frequency − 2 β e -2\beta_e − 2 β e satisfies the hypothesis of the lemma at scale R R R . Since R ≤ N R \le N R ≤ N , it also satisfies that hypothesis at scale N N N . Hence, if e e e is such an edge, every cut crossing e e e satisfies m N S , T ( − 2 β S , T ) ≪ Q − 1 / 2 m_N^{S,T}(-2\beta_{S,T}) \ll Q^{-1/2} m N S , T ( − 2 β S , T ) ≪ Q − 1/2 . Under the dimension hypothesis,
γ : = τ − ( K + 2 ) > 0. \gamma:= \tau- (K+2) > 0. γ := τ − ( K + 2 ) > 0.
We use the mean value estimate on the first 2 K + 4 2K+4 2 K + 4 powers and the pointwise estimate on the remaining 2 γ 2\gamma 2 γ powers. Finner’s inequality and (10) give
∫ m Q ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K d β ≪ K , d Q − γ N − 2 L . (18) \int_{\mathbf{m}_Q} \prod_{S|T\in\mathcal{B}_K} m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K}\,d\beta\ll_{K,d} Q^{-\gamma}N^{-2L}. \tag*{(18)} ∫ m Q S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K d β ≪ K , d Q − γ N − 2 L . (18)
It remains to explain why the major arcs retain useful combinatorial information. Let μ q \mu_q μ q be normalized counting measure on a long q q q -spaced box,
μ q = N q − d 1 { 0 , … , N q − 1 } d , N q : = ⌊ R q Q L ( L + 2 ) + 3 ⌋ . \mu_q=N_q^{-d}\mathbf{1}_{\{0,\ldots,N_q-1\}^{d}},\qquad N_q:=\left\lfloor\frac{R}{qQ^{L(L+2)+3}}\right\rfloor. μ q = N q − d 1 { 0 , … , N q − 1 } d , N q := ⌊ q Q L ( L + 2 ) + 3 R ⌋ .
Denote by Λ N m a j \Lambda_N^{\mathrm{maj}} Λ N maj the contribution to (6) from the major arcs. The major-arc estimate gives, for some η = η ( K , d ) > 1 \eta=\eta(K,d)>1 η = η ( K , d ) > 1 ,
∣ Λ N m a j ( F 0 , … , F K − 1 ) ∣ ≪ Δ , d R ( K − 1 ) d − 2 L ∑ q ≤ 2 Q L q − 1 − η ∏ i = 0 K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) \left|\Lambda_N^{\mathrm{maj}}(F_0,\ldots,F_{K-1})\right|
\ll_{\Delta,d} R^{(K-1)d-2L}\sum_{q\le2QL}q^{-1-\eta}\prod_{i=0}^{K-1}\|\mu_q*F_i\|_{\ell^K(\mathbb{Z}^d)} Λ N maj ( F 0 , … , F K − 1 ) ≪ Δ , d R ( K − 1 ) d − 2 L q ≤ 2 Q L ∑ q − 1 − η i = 0 ∏ K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d )
+ Q − 2 R K d / 2 − 2 L ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (19) \qquad+Q^{-2}R^{Kd/2-2L}\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. \tag*{(19)} + Q − 2 R K d /2 − 2 L i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (19)
The first term is the important one, as μ q ∗ g \mu_q*g μ q ∗ g gives information about the density of A A A on a q q q -spaced box.
Returning to (5) , choose Q Q Q to be a sufficiently large fixed power of α − 1 \alpha^{-1} α − 1 , multiplied by a sufficiently large constant. Unless N ≪ Δ , d α − C N\ll_{\Delta,d}\alpha^{-C} N ≪ Δ , d α − C , the scale conditions above hold. The minor-arc contribution bounded using (18) and the error term in (19) are then negligible. Since
∥ μ q ∗ 1 [ N ] d ∥ ℓ K ( Z d ) ≤ N d / K \|\mu_q * \mathbf{1}_{[N]^d}\|_{\ell^K(\mathbb{Z}^d)}\le N^{d/K} ∥ μ q ∗ 1 [ N ] d ∥ ℓ K ( Z d ) ≤ N d / K
and ∑ q ≥ 1 q − 1 − η < ∞ \sum_{q\ge1}q^{-1-\eta}<\infty ∑ q ≥ 1 q − 1 − η < ∞ , the main term in (19) implies that, for some q ≪ Δ , d α − C q\ll_{\Delta,d}\alpha^{-C} q ≪ Δ , d α − C ,
∑ x ∈ Z d ∣ μ q ∗ g ( x ) ∣ K ≫ Δ , d α K N d . \sum_{x\in\mathbb{Z}^d}|\mu_q*g(x)|^K\gg_{\Delta,d}\alpha^K N^d. x ∈ Z d ∑ ∣ μ q ∗ g ( x ) ∣ K ≫ Δ , d α K N d .
A standard positive-negative part argument, using μ q ∗ g ≥ − α \mu_q*g\ge-\alpha μ q ∗ g ≥ − α and ∑ x μ q ∗ g ( x ) = 0 \sum_x\mu_q*g(x)=0 ∑ x μ q ∗ g ( x ) = 0 , gives a point y ∈ [ 1 + q ( N q − 1 ) , N ] d y\in[1+q(N_q-1),N]^d y ∈ [ 1 + q ( N q − 1 ) , N ] d for which
( μ q ∗ 1 A ) ( y ) > ( 1 + κ ) α , (\mu_q*\mathbf{1}_A)(y)>(1+\kappa)\alpha, ( μ q ∗ 1 A ) ( y ) > ( 1 + κ ) α ,
where κ > 0 \kappa>0 κ > 0 depends only on Δ \Delta Δ and d d d . Thus either N ≪ Δ , d α − C N\ll_{\Delta,d}\alpha^{-C} N ≪ Δ , d α − C , or A A A has density at least ( 1 + κ ) α (1+\kappa)\alpha ( 1 + κ ) α on a d d d -dimensional progression of common difference q ≪ α − C q\ll\alpha^{-C} q ≪ α − C and side length N ′ ≫ α C N N'\gg\alpha^C N N ′ ≫ α C N . Translating and rescaling this progression produces another Δ \Delta Δ -free set of increased density. The standard iteration has O ( 1 + log ( 1 / α ) ) O(1+\log(1/\alpha)) O ( 1 + log ( 1/ α )) steps, each costing O ( 1 + log ( 1 / α ) ) O(1+\log(1/\alpha)) O ( 1 + log ( 1/ α )) in the logarithm of the scale. Hence
log N ≪ Δ , d ( 1 + log ( 1 / α ) ) 2 , \log N\ll_{\Delta,d}(1+\log(1/\alpha))^2, log N ≪ Δ , d ( 1 + log ( 1/ α ) ) 2 ,
which is the content of Theorem 1.1.
Further directions
The cut operator method may offer a route to quantitative results for more complex configurations. A natural test case is a simplex together with its barycenter, or more general barycentric configurations consisting of the vertices of a simplex together with the barycenters of all of its ℓ \ell ℓ -dimensional faces. We leave these configurations as a possible direction for future work.
Organization of the proof
Section 4 introduces the weighted counting form. Section 5 develops the cut reduction and proves the mean value estimate. Sections 6 and 7 establish the minor- and major-arc estimates. Section 8 proves and iterates the density increment. The lattice-flag count underlying Lemma 2.1 is proved in Appendix B.
Notation and standard definitions
We use the following notation throughout the paper.
N = { 1 , 2 , … } \mathbb{N} = \{1,2,\ldots\} N = { 1 , 2 , … } , [ N ] = { 1 , … , N } [N] = \{1,\ldots,N\} [ N ] = { 1 , … , N } for a positive integer N N N , and T = R / Z \mathbb{T} = \mathbb{R}/\mathbb{Z} T = R / Z . We write e ( x ) : = e 2 π i x e(x) := e^{2\pi i x} e ( x ) := e 2 π i x and e q ( x ) : = e ( x / q ) e_q(x) := e(x/q) e q ( x ) := e ( x / q ) .
For x ∈ R x \in\mathbb{R} x ∈ R , let ∥ x ∥ T : = min n ∈ Z ∣ x − n ∣ \lVert x\rVert_{\mathbb{T}} := \min_{n \in\mathbb{Z}} \lvert x-n\rvert ∥ x ∥ T := min n ∈ Z ∣ x − n ∣ . If x = ( x 1 , … , x m ) x = (x_1,\ldots,x_m) x = ( x 1 , … , x m ) lies in R m \mathbb{R}^m R m , then ∥ x ∥ ∞ : = max j ∣ x j ∣ \lVert x\rVert_\infty:= \max_j \lvert x_j\rvert ∥ x ∥ ∞ := max j ∣ x j ∣ , while for x ∈ T m x \in\mathbb{T}^m x ∈ T m we put ∥ x ∥ ∞ : = max j ∥ x j ∥ T \lVert x\rVert_\infty:= \max_j \lVert x_j\rVert_{\mathbb{T}} ∥ x ∥ ∞ := max j ∥ x j ∥ T . The notation diam ∞ ( E ) \operatorname{diam}_\infty(E) diam ∞ ( E ) refers to diameter with respect to the maximum norm.
For functions μ , F : G → C \mu,F : G \to\mathbb{C} μ , F : G → C , define their convolution by
( μ ∗ F ) ( y ) : = ∑ x ∈ G μ ( x ) F ( y − x ) . (\mu* F)(y) := \sum_{x \in G} \mu(x)F(y-x). ( μ ∗ F ) ( y ) := x ∈ G ∑ μ ( x ) F ( y − x ) .
If T : H 1 → H 2 T : H_1 \to H_2 T : H 1 → H 2 is a bounded linear operator between Hilbert spaces, its adjoint T ∗ : H 2 → H 1 T^* : H_2 \to H_1 T ∗ : H 2 → H 1 is determined by ⟨ T f , g ⟩ H 2 = ⟨ f , T ∗ g ⟩ H 1 \langle T f,g\rangle_{H_2} = \langle f,T^*g\rangle_{H_1} ⟨ T f , g ⟩ H 2 = ⟨ f , T ∗ g ⟩ H 1 . We define
∥ T ∥ 2 → 2 : = sup f ∈ H 1 ∥ f ∥ H 1 = 1 ∥ T f ∥ H 2 . \lVert T\rVert_{2 \to2} := \sup_{\substack{f \in H_1 \\ \lVert f\rVert_{H_1}=1}} \lVert Tf\rVert_{H_2}. ∥ T ∥ 2 → 2 := f ∈ H 1 ∥ f ∥ H 1 = 1 sup ∥ T f ∥ H 2 .
If ( e j ) j (e_j)_j ( e j ) j is any orthonormal basis of H 1 H_1 H 1 , then
∥ T ∥ H S : = ( ∑ j ∥ T e j ∥ H 2 2 ) 1 / 2 . \lVert T\rVert_{\mathrm{HS}} := \left(\sum_j \lVert T e_j\rVert_{H_2}^2\right)^{1/2}. ∥ T ∥ HS := ( j ∑ ∥ T e j ∥ H 2 2 ) 1/2 .
This value is independent of the choice of orthonormal basis.
If a ∈ ( Z / q Z ) m a \in(\mathbb{Z}/q\mathbb{Z})^m a ∈ ( Z / q Z ) m , then ( a , q ) = 1 (a,q)=1 ( a , q ) = 1 means that the coordinates of a a a together with q q q have no common divisor greater than 1.
We use X ≪ Y X \ll Y X ≪ Y and X = O ( Y ) X = O(Y) X = O ( Y ) interchangeably to mean that ∣ X ∣ ≤ C Y \lvert X\rvert\le CY ∣ X ∣ ≤ C Y for some constant C > 0 C > 0 C > 0 . We write X ≫ Y X \gg Y X ≫ Y if Y ≪ X Y \ll X Y ≪ X , and X ≍ Y X \asymp Y X ≍ Y if both X ≪ Y X \ll Y X ≪ Y and Y ≪ X Y \ll X Y ≪ X . Subscripts indicate the permitted dependence of the implied constants.
Recall the definition of Δ \Delta Δ from the introduction, and suppose that A ⊆ [ N ] d A \subseteq[N]^d A ⊆ [ N ] d contains no copy similar to Δ \Delta Δ . In this section we define a weighted count for copies of Δ \Delta Δ , extract a large term involving the balanced function, and then express the count by Fourier inversion.
The simplex equations
Put L : = ( K 2 ) L := \binom{K}{2} L := ( 2 K ) . Let
E : = { { i , j } : 0 ≤ i < j ≤ K − 1 } , ∣ E ∣ = L . \mathcal{E} := \{\{i,j\}: 0 \le i < j \le K-1\}, \qquad|\mathcal{E}| = L. E := {{ i , j } : 0 ≤ i < j ≤ K − 1 } , ∣ E ∣ = L .
We count configurations z = ( z 0 , … , z K − 1 ) ∈ ( Z d ) K z=(z_0,\ldots,z_{K-1}) \in(\mathbb{Z}^d)^K z = ( z 0 , … , z K − 1 ) ∈ ( Z d ) K satisfying
∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( ∀ { i , j } ∈ E ) (20) |z_i-z_j|^2=n|v_i-v_j|^2 \qquad(\forall\{i,j\}\in\mathcal{E}) \tag*{(20)} ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( ∀ { i , j } ∈ E ) (20)
for some integer n > 0 n>0 n > 0 . These equations say that the points z 0 , … , z K − 1 z_0,\ldots,z_{K-1} z 0 , … , z K − 1 form a similar copy of Δ \Delta Δ with dilation factor n \sqrt{n} n . It is enough to consider integer values of the squared dilation factor: a set containing no similar copy of Δ \Delta Δ contains, in particular, no such copy with integer squared dilation factor. Moreover, Kitaoka’s simplex count, which we apply shortly, counts precisely the copies occurring at these scales.
For an integer R ≥ 1 R \ge1 R ≥ 1 , define
w R ( n ) : = R − 2 1 { R 2 ≤ n < 2 R 2 } , n ∈ Z . w_R(n):=R^{-2}1_{\{R^2\le n<2R^2\}}, \qquad n\in\mathbb{Z}. w R ( n ) := R − 2 1 { R 2 ≤ n < 2 R 2 } , n ∈ Z .
Thus w R w_R w R restricts the squared dilation factor to n ≍ R 2 n\asymp R^2 n ≍ R 2 and ∑ n w R ( n ) = 1 \sum_n w_R(n)=1 ∑ n w R ( n ) = 1 . Put
D Δ : = max { i , j } ∈ E ∣ v i − v j ∣ , D 0 : = 1 + ⌊ 2 D Δ ⌋ . D_\Delta:=\max_{\{i,j\}\in\mathcal{E}}|v_i-v_j|,\qquad D_0:=1+\lfloor\sqrt{2}D_\Delta\rfloor. D Δ := { i , j } ∈ E max ∣ v i − v j ∣ , D 0 := 1 + ⌊ 2 D Δ ⌋ .
Whenever (20) holds and w R ( n ) ≠ 0 w_R(n)\ne0 w R ( n ) = 0 , we have
∣ z i − z j ∣ = n ∣ v i − v j ∣ < 2 R D Δ < D 0 R ( 0 ≤ i < j ≤ K − 1 ) . (21) |z_i-z_j|=\sqrt{n}|v_i-v_j|<\sqrt{2}RD_\Delta<D_0R \qquad(0\le i<j\le K-1). \tag*{(21)} ∣ z i − z j ∣ = n ∣ v i − v j ∣ < 2 R D Δ < D 0 R ( 0 ≤ i < j ≤ K − 1 ) . (21)
Fix a constant 0 < σ < 1 / ( 4 D 0 ) 0<\sigma<1/(4D_0) 0 < σ < 1/ ( 4 D 0 ) , depending only on Δ \Delta Δ . Choose a fixed threshold N 0 = N 0 ( Δ , d ) ≥ 2 / σ N_0=N_0(\Delta,d)\ge2/\sigma N 0 = N 0 ( Δ , d ) ≥ 2/ σ , large enough for the representation estimate (Lemma 4.1) used below. After enlarging the theorem’s implicit constant, we may assume N ≥ N 0 N\ge N_0 N ≥ N 0 ; otherwise the conclusion is immediate. Put
R = ⌊ σ N ⌋ , α = ∣ A ∣ N d . (22) R=\lfloor\sigma N\rfloor,\qquad\alpha=\frac{|A|}{N^d}. \tag*{(22)} R = ⌊ σ N ⌋ , α = N d ∣ A ∣ . (22)
The weighted count and the balanced function
For finitely supported functions F 0 , … , F K − 1 : Z d → C F_0,\ldots,F_{K-1}:\mathbb{Z}^d\to\mathbb{C} F 0 , … , F K − 1 : Z d → C , define the simplex counting form
Λ N ( F 0 , … , F K − 1 ) : = ∑ z 0 , … , z K − 1 ∈ Z d n ∈ Z w R ( n ) 1 { ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( ∀ { i , j } ∈ E ) } ∏ i = 0 K − 1 F i ( z i ) . (23) \Lambda_N(F_0,\ldots,F_{K-1}):=
\sum_{\substack{z_0,\ldots,z_{K-1}\in\mathbb{Z}^d\\ n\in\mathbb{Z}}}
w_R(n)1_{\{|z_i-z_j|^2=n|v_i-v_j|^2\;(\forall\{i,j\}\in\mathcal{E})\}}
\prod_{i=0}^{K-1}F_i(z_i). \tag*{(23)} Λ N ( F 0 , … , F K − 1 ) := z 0 , … , z K − 1 ∈ Z d n ∈ Z ∑ w R ( n ) 1 { ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( ∀ { i , j } ∈ E )} i = 0 ∏ K − 1 F i ( z i ) . (23)
Lemma 4.1 (Kitaoka’s simplex count). If d ≥ 4 K + 4 d\ge4K+4 d ≥ 4 K + 4 and N ≥ N 0 N\ge N_0 N ≥ N 0 , then
Λ N ( 1 [ N ] d , … , 1 [ N ] d ) ≫ Δ , d N d R ( K − 1 ) d − 2 L . (24) \Lambda_N(1_{[N]^d},\ldots,1_{[N]^d})\gg_{\Delta,d}N^dR^{(K-1)d-2L}. \tag*{(24)} Λ N ( 1 [ N ] d , … , 1 [ N ] d ) ≫ Δ , d N d R ( K − 1 ) d − 2 L . (24)
Proof. Put m 0 = 0 m_0=0 m 0 = 0 , and let
I N : = { x ∈ [ N ] d : 1 + D 0 R ≤ x j ≤ N − D 0 R for 1 ≤ j ≤ d } . I_N:=\left\{x\in[N]^d:1+D_0R\le x_j\le N-D_0R\text{ for }1\le j\le d\right\}. I N := { x ∈ [ N ] d : 1 + D 0 R ≤ x j ≤ N − D 0 R for 1 ≤ j ≤ d } .
Since D 0 R < N / 4 D_0R<N/4 D 0 R < N /4 , we have ∣ I N ∣ ≫ d N d |I_N|\gg_d N^d ∣ I N ∣ ≫ d N d .
Fix x ∈ I N x\in I_N x ∈ I N , and write z i = x + m i z_i=x+m_i z i = x + m i . Since z i − z j = m i − m j z_i-z_j=m_i-m_j z i − z j = m i − m j , the configuration equations are
∣ m i − m j ∣ 2 = n ∣ v i − v j ∣ 2 ( { i , j } ∈ E ) . |m_i-m_j|^2=n|v_i-v_j|^2\qquad(\{i,j\}\in\mathcal{E}). ∣ m i − m j ∣ 2 = n ∣ v i − v j ∣ 2 ({ i , j } ∈ E ) .
Because m 0 = 0 m_0=0 m 0 = 0 , simple rearranging shows that these equations are equivalent to
m i ⋅ m j = n ( v i − v 0 ) ⋅ ( v j − v 0 ) ( 1 ≤ i , j ≤ K − 1 ) . m_i\cdot m_j=n(v_i-v_0)\cdot(v_j-v_0)\qquad(1\le i,j\le K-1). m i ⋅ m j = n ( v i − v 0 ) ⋅ ( v j − v 0 ) ( 1 ≤ i , j ≤ K − 1 ) .
Kitaoka’s estimate [13 ] , in the form recorded in [17 ] , therefore gives, whenever R 2 ≤ n < 2 R 2 R^2\le n<2R^2 R 2 ≤ n < 2 R 2 ,
# { ( m 1 , … , m K − 1 ) ∈ ( Z d ) K − 1 : ∣ m i − m j ∣ 2 = n ∣ v i − v j ∣ 2 for every { i , j } ∈ E } ≫ Δ , d n ( K − 1 ) ( d − K ) / 2 . \#\left\{(m_1,\ldots,m_{K-1})\in(\mathbb{Z}^d)^{K-1}:
\begin{array}{c}
|m_i-m_j|^2=n|v_i-v_j|^2\\
\text{for every }\{i,j\}\in\mathcal{E}
\end{array}
\right\}\gg_{\Delta,d}n^{(K-1)(d-K)/2}. # { ( m 1 , … , m K − 1 ) ∈ ( Z d ) K − 1 : ∣ m i − m j ∣ 2 = n ∣ v i − v j ∣ 2 for every { i , j } ∈ E } ≫ Δ , d n ( K − 1 ) ( d − K ) /2 .
By (21) , every such m i m_i m i satisfies ∣ m i ∣ < D 0 R |m_i|<D_0R ∣ m i ∣ < D 0 R , so x + m i ∈ [ N ] d x+m_i\in[N]^d x + m i ∈ [ N ] d for every x ∈ I N x\in I_N x ∈ I N . There are R 2 R^2 R 2 integers n n n in this range, and w R ( n ) = R − 2 w_R(n)=R^{-2} w R ( n ) = R − 2 for each of them. Hence
Λ N ( 1 [ N ] d , … , 1 [ N ] d ) ≫ Δ , d N d ⋅ R 2 ⋅ R − 2 ⋅ R ( K − 1 ) ( d − K ) = N d R ( K − 1 ) d − 2 L = N d R ( K − 1 ) d − 2 L . \Lambda_N(1_{[N]^d},\ldots,1_{[N]^d})\gg_{\Delta,d}N^d\cdot R^2\cdot R^{-2}\cdot R^{(K-1)(d-K)}=N^dR^{(K-1)d-2L}=N^dR^{(K-1)d-2L}. Λ N ( 1 [ N ] d , … , 1 [ N ] d ) ≫ Δ , d N d ⋅ R 2 ⋅ R − 2 ⋅ R ( K − 1 ) ( d − K ) = N d R ( K − 1 ) d − 2 L = N d R ( K − 1 ) d − 2 L .
Since A A A contains no similar copy of Δ \Delta Δ , we immediately have
Λ N ( 1 A , … , 1 A ) = 0. (25) \Lambda_N(1_A,\ldots,1_A)=0. \tag*{(25)} Λ N ( 1 A , … , 1 A ) = 0. (25)
We now extract an important consequence of (25) . There is nothing to prove if A = ∅ A=\varnothing A = ∅ , so assume that α > 0 \alpha>0 α > 0 and write g : = 1 A − α 1 [ N ] d g:=1_A-\alpha1_{[N]^d} g := 1 A − α 1 [ N ] d . Then
∑ x ∈ Z d g ( x ) = 0 , ∣ g ∣ ≤ 1 , ∥ g ∥ ℓ 2 ( Z d ) 2 = N d α ( 1 − α ) ≤ α N d . (26) \sum_{x\in\mathbb{Z}^d}g(x)=0,\qquad|g|\le1,\qquad\|g\|_{\ell^2(\mathbb{Z}^d)}^2=N^d\alpha(1-\alpha)\le\alpha N^d. \tag*{(26)} x ∈ Z d ∑ g ( x ) = 0 , ∣ g ∣ ≤ 1 , ∥ g ∥ ℓ 2 ( Z d ) 2 = N d α ( 1 − α ) ≤ α N d . (26)
For each C ⊆ { 0 , … , K − 1 } C\subseteq\{0,\ldots,K-1\} C ⊆ { 0 , … , K − 1 } , let F i C = g F_i^C=g F i C = g if i ∈ C i\in C i ∈ C and F i C = 1 [ N ] d F_i^C=1_{[N]^d} F i C = 1 [ N ] d otherwise. Multilinearity and (25) give the exact expansion
0 = ∑ C ⊆ { 0 , … , K − 1 } α K − ∣ C ∣ Λ N ( F 0 C , … , F K − 1 C ) . (27) 0=\sum_{C\subseteq\{0,\ldots,K-1\}}\alpha^{K-|C|}\Lambda_N(F_0^C,\ldots,F_{K-1}^C). \tag*{(27)} 0 = C ⊆ { 0 , … , K − 1 } ∑ α K − ∣ C ∣ Λ N ( F 0 C , … , F K − 1 C ) . (27)
The term with C = ∅ C=\varnothing C = ∅ is α K Λ N ( 1 [ N ] d , … , 1 [ N ] d ) \alpha^K\Lambda_N(1_{[N]^d},\ldots,1_{[N]^d}) α K Λ N ( 1 [ N ] d , … , 1 [ N ] d ) . Moving this term to the other side, taking absolute values, and using (24) , we obtain
∑ C ⊆ { 0 , … , K − 1 } C ≠ ∅ α K − ∣ C ∣ ∣ Λ N ( F 0 C , … , F K − 1 C ) ∣ ≫ Δ , d α K N d R ( K − 1 ) d − 2 L . \sum_{\substack{C\subseteq\{0,\ldots,K-1\}\\C\ne\varnothing}}\alpha^{K-|C|}\left|\Lambda_N(F_0^C,\ldots,F_{K-1}^C)\right|\gg_{\Delta,d}\alpha^KN^dR^{(K-1)d-2L}. C ⊆ { 0 , … , K − 1 } C = ∅ ∑ α K − ∣ C ∣ Λ N ( F 0 C , … , F K − 1 C ) ≫ Δ , d α K N d R ( K − 1 ) d − 2 L .
There are only 2 K − 1 2^K-1 2 K − 1 terms in this sum. Hence some nonempty B ⊆ { 0 , … , K − 1 } B\subseteq\{0,\ldots,K-1\} B ⊆ { 0 , … , K − 1 } , with s : = ∣ B ∣ s:=|B| s := ∣ B ∣ satisfying 1 ≤ s ≤ K 1\le s\le K 1 ≤ s ≤ K , has the property that, writing F i = F i B F_i=F_i^B F i = F i B ,
∣ Λ N ( F 0 , … , F K − 1 ) ∣ ≫ Δ , d α s N d R ( K − 1 ) d − 2 L . (28) |\Lambda_N(F_0,\ldots,F_{K-1})|\gg_{\Delta,d}\alpha^sN^dR^{(K-1)d-2L}. \tag*{(28)} ∣ Λ N ( F 0 , … , F K − 1 ) ∣ ≫ Δ , d α s N d R ( K − 1 ) d − 2 L . (28)
where F i = g F_i=g F i = g for i ∈ B i\in B i ∈ B and F i = 1 [ N ] d F_i=1_{[N]^d} F i = 1 [ N ] d otherwise.
Applying the orthogonality relation
For β = ( β i j ) { i , j } ∈ E ∈ T L \beta=(\beta_{ij})_{\{i,j\}\in\mathcal{E}}\in\mathbb{T}^{L} β = ( β ij ) { i , j } ∈ E ∈ T L , we use the convention β j i = β i j \beta_{ji}=\beta_{ij} β j i = β ij . For a tuple u = ( u 0 , … , u K − 1 ) u=(u_{0},\ldots,u_{K-1}) u = ( u 0 , … , u K − 1 ) , set
Q β ( u ) : = ∑ { i , j } ∈ E β i j ∣ u i − u j ∣ 2 . Q_{\beta}(u):=\sum_{\{i,j\}\in\mathcal{E}}\beta_{ij}|u_{i}-u_{j}|^{2}. Q β ( u ) := { i , j } ∈ E ∑ β ij ∣ u i − u j ∣ 2 .
We apply the orthogonality relation to detect solutions to the L L L equations in the definition of Λ N \Lambda_{N} Λ N , writing each equation as ∣ z i − z j ∣ 2 − n ∣ v i − v j ∣ 2 = 0 |z_{i}-z_{j}|^{2}-n|v_{i}-v_{j}|^{2}=0 ∣ z i − z j ∣ 2 − n ∣ v i − v j ∣ 2 = 0 . If z = ( z 0 , … , z K − 1 ) z=(z_{0},\ldots,z_{K-1}) z = ( z 0 , … , z K − 1 ) and v = ( v 0 , … , v K − 1 ) v=(v_{0},\ldots,v_{K-1}) v = ( v 0 , … , v K − 1 ) , the resulting phase is
∑ { i , j } ∈ E β i j ( ∣ z i − z j ∣ 2 − n ∣ v i − v j ∣ 2 ) = Q β ( z ) − n Q β ( v ) . \sum_{\{i,j\}\in\mathcal{E}}\beta_{ij}\left(|z_{i}-z_{j}|^{2}-n|v_{i}-v_{j}|^{2}\right)=Q_{\beta}(z)-nQ_{\beta}(v). { i , j } ∈ E ∑ β ij ( ∣ z i − z j ∣ 2 − n ∣ v i − v j ∣ 2 ) = Q β ( z ) − n Q β ( v ) .
Thus
1 ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( ∀ { i , j } ∈ E ) = ∫ T L e ( Q β ( z ) − n Q β ( v ) ) d β . \mathbf{1}_{|z_{i}-z_{j}|^{2}=n|v_{i}-v_{j}|^{2}\ (\forall\{i,j\}\in\mathcal{E})}=\int_{\mathbb{T}^{L}}e\left(Q_{\beta}(z)-nQ_{\beta}(v)\right)\,d\beta. 1 ∣ z i − z j ∣ 2 = n ∣ v i − v j ∣ 2 ( ∀ { i , j } ∈ E ) = ∫ T L e ( Q β ( z ) − n Q β ( v ) ) d β .
Substituting this identity into the counting form gives
Λ N ( F 0 , … , F K − 1 ) = ∫ T L ∑ n ∈ Z w R ( n ) e ( − n Q β ( v ) ) ∑ z 0 , … , z K − 1 ∈ Z d e ( Q β ( z ) ) ∏ i = 0 K − 1 F i ( z i ) d β . (29) \Lambda_{N}(F_{0},\ldots,F_{K-1})=\int_{\mathbb{T}^{L}}\sum_{n\in\mathbb{Z}}w_{R}(n)e\left(-nQ_{\beta}(v)\right)\sum_{z_{0},\ldots,z_{K-1}\in\mathbb{Z}^{d}}e\left(Q_{\beta}(z)\right)\prod_{i=0}^{K-1}F_{i}(z_{i})\,d\beta. \tag*{(29)} Λ N ( F 0 , … , F K − 1 ) = ∫ T L n ∈ Z ∑ w R ( n ) e ( − n Q β ( v ) ) z 0 , … , z K − 1 ∈ Z d ∑ e ( Q β ( z ) ) i = 0 ∏ K − 1 F i ( z i ) d β . (29)
Observe that the the sum over n n n in (29) is w ^ R ( Q β ( v ) ) \widehat{w}_{R}(Q_{\beta}(v)) w R ( Q β ( v )) . For β ∈ T L \beta\in\mathbb{T}^{L} β ∈ T L , write
S β ( F 0 , … , F K − 1 ) : = ∑ z 0 , … , z K − 1 ∈ Z d e ( Q β ( z ) ) ∏ i = 0 K − 1 F i ( z i ) . (30) S_{\beta}(F_{0},\ldots,F_{K-1}):=\sum_{z_{0},\ldots,z_{K-1}\in\mathbb{Z}^{d}}e\left(Q_{\beta}(z)\right)\prod_{i=0}^{K-1}F_{i}(z_{i}). \tag*{(30)} S β ( F 0 , … , F K − 1 ) := z 0 , … , z K − 1 ∈ Z d ∑ e ( Q β ( z ) ) i = 0 ∏ K − 1 F i ( z i ) . (30)
We write S β S_{\beta} S β when the inputs are clear. Since every F i F_{i} F i is supported on [ N ] d [N]^{d} [ N ] d , the sum in (30) is finite and may equivalently be taken over z 0 , … , z K − 1 ∈ [ N ] d z_{0},\ldots,z_{K-1}\in[N]^{d} z 0 , … , z K − 1 ∈ [ N ] d . For a measurable set B ⊆ T L \mathcal{B}\subseteq\mathbb{T}^{L} B ⊆ T L , define
Λ N B ( F 0 , … , F K − 1 ) : = ∫ B w ^ R ( Q β ( v ) ) S β d β . (31) \Lambda_{N}^{\mathcal{B}}(F_{0},\ldots,F_{K-1}):=\int_{\mathcal{B}}\widehat{w}_{R}(Q_{\beta}(v))S_{\beta}\,d\beta. \tag*{(31)} Λ N B ( F 0 , … , F K − 1 ) := ∫ B w R ( Q β ( v )) S β d β . (31)
Taking B = T L \mathcal{B}=\mathbb{T}^{L} B = T L gives Λ N = Λ N T L \Lambda_{N}=\Lambda_{N}^{\mathbb{T}^{L}} Λ N = Λ N T L .
Since w R w_{R} w R is nonnegative and ∑ n w R ( n ) = 1 \sum_{n}w_{R}(n)=1 ∑ n w R ( n ) = 1 ,
∥ w ^ R ∥ L ∞ ( T ) ≤ 1. (32) \lVert\widehat{w}_{R}\rVert_{L^{\infty}(\mathbb{T})}\leq1. \tag*{(32)} ∥ w R ∥ L ∞ ( T ) ≤ 1. (32)
Cut operators
By (31) , both the major arc and minor arc estimates reduce to bounds for the exponential sum
S β ( F 0 , … , F K − 1 ) = ∑ z 0 , … , z K − 1 ∈ [ N ] d e ( Q β ( z ) ) ∏ i = 0 K − 1 F i ( z i ) , S_{\beta}(F_{0},\ldots,F_{K-1})=\sum_{z_{0},\ldots,z_{K-1}\in[N]^{d}}e\left(Q_{\beta}(z)\right)\prod_{i=0}^{K-1}F_{i}(z_{i}), S β ( F 0 , … , F K − 1 ) = z 0 , … , z K − 1 ∈ [ N ] d ∑ e ( Q β ( z ) ) i = 0 ∏ K − 1 F i ( z i ) ,
where the phase is
Q β ( z ) = ∑ { i , j } ∈ E β i j ∣ z i − z j ∣ 2 . Q_{\beta}(z)=\sum_{\{i,j\}\in\mathcal{E}}\beta_{ij}|z_{i}-z_{j}|^{2}. Q β ( z ) = { i , j } ∈ E ∑ β ij ∣ z i − z j ∣ 2 .
We estimate this sum by partitioning the variables into two blocks and treating the part of the phase coupling those blocks as an operator kernel.
The cut operator reduction
A cut S ∣ T S \mid T S ∣ T is a partition of { 0 , … , K − 1 } \{0,\ldots,K-1\} { 0 , … , K − 1 } into two nonempty sets. Fix such a cut, and for z = ( z 0 , … , z K − 1 ) z=(z_0,\ldots,z_{K-1}) z = ( z 0 , … , z K − 1 ) , write
z S : = ( z i ) i ∈ S , z T : = ( z j ) j ∈ T z_S := (z_i)_{i\in S},\qquad z_T := (z_j)_{j\in T} z S := ( z i ) i ∈ S , z T := ( z j ) j ∈ T .
An edge contained entirely in S S S or entirely in T T T contributes to Q β ( z ) Q_\beta(z) Q β ( z ) a term depending on only one of these two blocks. For a crossing edge, with i ∈ S i\in S i ∈ S and j ∈ T j\in T j ∈ T , we have
β i j ∣ z i − z j ∣ 2 = β i j ∣ z i ∣ 2 − 2 β i j z i ⋅ z j + β i j ∣ z j ∣ 2 . \beta_{ij}|z_i-z_j|^2=\beta_{ij}|z_i|^2-2\beta_{ij}z_i\cdot z_j+\beta_{ij}|z_j|^2. β ij ∣ z i − z j ∣ 2 = β ij ∣ z i ∣ 2 − 2 β ij z i ⋅ z j + β ij ∣ z j ∣ 2 .
Collecting the terms according to the variables on which they depend, define
Q β S ( z S ) : = ∑ { i , i ′ } ∈ E i , i ′ ∈ S β i i ′ ∣ z i − z i ′ ∣ 2 + ∑ i ∈ S , j ∈ T β i j ∣ z i ∣ 2 , Q_\beta^S(z_S):=\sum_{\substack{\{i,i'\}\in E\\i,i'\in S}}\beta_{ii'}|z_i-z_{i'}|^2+\sum_{\substack{i\in S,j\in T}}\beta_{ij}|z_i|^2, Q β S ( z S ) := { i , i ′ } ∈ E i , i ′ ∈ S ∑ β i i ′ ∣ z i − z i ′ ∣ 2 + i ∈ S , j ∈ T ∑ β ij ∣ z i ∣ 2 ,
Q β S , T ( z S , z T ) : = − 2 ∑ i ∈ S , j ∈ T β i j z i ⋅ z j , Q_\beta^{S,T}(z_S,z_T):=-2\sum_{\substack{i\in S,j\in T}}\beta_{ij}z_i\cdot z_j, Q β S , T ( z S , z T ) := − 2 i ∈ S , j ∈ T ∑ β ij z i ⋅ z j ,
Q β T ( z T ) : = ∑ { j , j ′ } ∈ E j , j ′ ∈ T β j j ′ ∣ z j − z j ′ ∣ 2 + ∑ i ∈ S , j ∈ T β i j ∣ z j ∣ 2 . Q_\beta^T(z_T):=\sum_{\substack{\{j,j'\}\in E\\j,j'\in T}}\beta_{jj'}|z_j-z_{j'}|^2+\sum_{\substack{i\in S,j\in T}}\beta_{ij}|z_j|^2. Q β T ( z T ) := { j , j ′ } ∈ E j , j ′ ∈ T ∑ β j j ′ ∣ z j − z j ′ ∣ 2 + i ∈ S , j ∈ T ∑ β ij ∣ z j ∣ 2 .
Then
Q β ( z ) = Q β S ( z S ) + Q β S , T ( z S , z T ) + Q β T ( z T ) . (33) Q_\beta(z)=Q_\beta^S(z_S)+Q_\beta^{S,T}(z_S,z_T)+Q_\beta^T(z_T). \tag*{(33)} Q β ( z ) = Q β S ( z S ) + Q β S , T ( z S , z T ) + Q β T ( z T ) . (33)
The first and third terms in (33) depend on variables from only one side of the cut, while the middle term is the only one involving variables from both sides. We view e ( Q β S , T ( z S , z T ) ) e\left(Q_\beta^{S,T}(z_S,z_T)\right) e ( Q β S , T ( z S , z T ) ) as the kernel of an operator taking functions of z T z_T z T to functions of z S z_S z S . The cut-reduction lemma below shows that the two one-sided phases e ( Q β S ( z S ) ) e\left(Q_\beta^S(z_S)\right) e ( Q β S ( z S ) ) and e ( Q β T ( z T ) ) e\left(Q_\beta^T(z_T)\right) e ( Q β T ( z T ) ) in (33) act by unitary multiplication and therefore do not affect the relevant 2 → 2 2\to2 2 → 2 operator norm.
The cut operator reduction will be applied in two different measure-space settings. On the minor arcs, ( X i , μ i ) = ( [ N ] d , # ) (X_i,\mu_i)=([N]^d,\#) ( X i , μ i ) = ([ N ] d , # ) , where # \# # denotes counting measure. On the major arcs, X i = B i × ( Z / q Z ) d X_i=B_i\times(\mathbb{Z}/q\mathbb{Z})^d X i = B i × ( Z / q Z ) d , where B i ⊆ Z d B_i\subseteq\mathbb{Z}^d B i ⊆ Z d is finite, and we equip X i X_i X i with the measure μ i \mu_i μ i determined by
∫ X i f d μ i = E r i m o d q ∑ u i ∈ B i f ( u i , r i ) . \int_{X_i} f\,\mathrm{d}\mu_i=\mathbb{E}_{r_i\mathbin{\mathrm{mod}}q}\sum_{u_i\in B_i}f(u_i,r_i). ∫ X i f d μ i = E r i mod q u i ∈ B i ∑ f ( u i , r i ) .
Therefore, we formulate the reduction for arbitrary finite measure spaces out of convenience. For 0 ≤ i ≤ K − 1 0\leq i\leq K-1 0 ≤ i ≤ K − 1 , let ( X i , μ i ) (X_i,\mu_i) ( X i , μ i ) be a finite measure space and put
X S : = ∏ i ∈ S X i , X T : = ∏ j ∈ T X j , μ S : = ⨂ i ∈ S μ i , μ T : = ⨂ j ∈ T μ j . X_S:=\prod_{i\in S}X_i,\qquad X_T:=\prod_{j\in T}X_j,\qquad\mu_S:=\bigotimes_{i\in S}\mu_i,\qquad\mu_T:=\bigotimes_{j\in T}\mu_j. X S := i ∈ S ∏ X i , X T := j ∈ T ∏ X j , μ S := i ∈ S ⨂ μ i , μ T := j ∈ T ⨂ μ j .
Given a measurable function Q S , T : X S × X T ⟶ T Q^{S,T}:X_S\times X_T\longrightarrow\mathbb{T} Q S , T : X S × X T ⟶ T , define
K S , T : L 2 ( X T , μ T ) ⟶ L 2 ( X S , μ S ) , \mathcal{K}^{S,T}:L^2(X_T,\mu_T)\longrightarrow L^2(X_S,\mu_S), K S , T : L 2 ( X T , μ T ) ⟶ L 2 ( X S , μ S ) ,
( K S , T h ) ( x S ) : = ∫ X T e ( Q S , T ( x S , x T ) ) h ( x T ) d μ T ( x T ) . (\mathcal{K}^{S,T}h)(x_S):=\int_{X_T}e\left(Q^{S,T}(x_S,x_T)\right)h(x_T)\,\mathrm{d}\mu_T(x_T). ( K S , T h ) ( x S ) := ∫ X T e ( Q S , T ( x S , x T ) ) h ( x T ) d μ T ( x T ) .
We call every operator of this form a cut operator .
Lemma 5.1 (Cut-operator reduction). Let S ∣ T S \mid T S ∣ T , ( X i , μ i ) (X_i,\mu_i) ( X i , μ i ) , Q S , T Q^{S,T} Q S , T , and K S , T K^{S,T} K S , T be as above. Let
Q S : X S → T , Q T : X T → T Q^S : X_S \to\mathbb{T}, \qquad Q^T : X_T \to\mathbb{T} Q S : X S → T , Q T : X T → T
be measurable. Then, for f i ∈ L 2 ( X i , μ i ) f_i \in L^2(X_i,\mu_i) f i ∈ L 2 ( X i , μ i ) ,
∣ ∫ X S ∫ X T e ( Q S ( x S ) + Q S , T ( x S , x T ) + Q T ( x T ) ) ∏ i = 0 K − 1 f i ( x i ) d μ T ( x T ) d μ S ( x S ) ∣ ≤ ∥ K S , T ∥ 2 → 2 ∏ i = 0 K − 1 ∥ f i ∥ L 2 ( X i , μ i ) . (34) \left|\int_{X_S}\int_{X_T} e\left(Q^S(x_S)+Q^{S,T}(x_S,x_T)+Q^T(x_T)\right)\prod_{i=0}^{K-1} f_i(x_i)\,d\mu_T(x_T)\,d\mu_S(x_S)\right|
\le\left\|K^{S,T}\right\|_{2\to2}\prod_{i=0}^{K-1}\left\|f_i\right\|_{L^2(X_i,\mu_i)}. \tag*{(34)} ∫ X S ∫ X T e ( Q S ( x S ) + Q S , T ( x S , x T ) + Q T ( x T ) ) i = 0 ∏ K − 1 f i ( x i ) d μ T ( x T ) d μ S ( x S ) ≤ K S , T 2 → 2 i = 0 ∏ K − 1 ∥ f i ∥ L 2 ( X i , μ i ) . (34)
Proof. Define multiplication operators by
( U S h ) ( x S ) : = e ( Q S ( x S ) ) h ( x S ) , ( U T h ) ( x T ) : = e ( Q T ( x T ) ) h ( x T ) . (U_S h)(x_S):=e\left(Q^S(x_S)\right)h(x_S), \qquad(U_T h)(x_T):=e\left(Q^T(x_T)\right)h(x_T). ( U S h ) ( x S ) := e ( Q S ( x S ) ) h ( x S ) , ( U T h ) ( x T ) := e ( Q T ( x T ) ) h ( x T ) .
Both operators are unitary.
For every h ∈ L 2 ( X T , μ T ) h\in L^2(X_T,\mu_T) h ∈ L 2 ( X T , μ T ) ,
( U S K S , T U T h ) ( x S ) = e ( Q S ( x S ) ) ∫ X T e ( Q S , T ( x S , x T ) ) e ( Q T ( x T ) ) h ( x T ) d μ T ( x T ) = ∫ X T e ( Q S ( x S ) + Q S , T ( x S , x T ) + Q T ( x T ) ) h ( x T ) d μ T ( x T ) . \begin{aligned}
(U_S K^{S,T}U_T h)(x_S)=e\left(Q^S(x_S)\right)\int_{X_T}e\left(Q^{S,T}(x_S,x_T)\right)e\left(Q^T(x_T)\right)h(x_T)\,d\mu_T(x_T) \\
&=\int_{X_T}e\left(Q^S(x_S)+Q^{S,T}(x_S,x_T)+Q^T(x_T)\right)h(x_T)\,d\mu_T(x_T).
\end{aligned} ( U S K S , T U T h ) ( x S ) = e ( Q S ( x S ) ) ∫ X T e ( Q S , T ( x S , x T ) ) e ( Q T ( x T ) ) h ( x T ) d μ T ( x T ) = ∫ X T e ( Q S ( x S ) + Q S , T ( x S , x T ) + Q T ( x T ) ) h ( x T ) d μ T ( x T ) .
Thus U S K S , T U T U_SK^{S,T}U_T U S K S , T U T is exactly the operator with the full phase. Since U S U_S U S and U T U_T U T are unitary,
∥ U S K S , T U T ∥ 2 → 2 = ∥ K S , T ∥ 2 → 2 . \left\|U_SK^{S,T}U_T\right\|_{2\to2}=\left\|K^{S,T}\right\|_{2\to2}. U S K S , T U T 2 → 2 = K S , T 2 → 2 .
The integral in the statement can now be written as
∫ X S ( ∏ i ∈ S f i ( x i ) ) ( U S K S , T U T [ ∏ j ∈ T f j ] ) ( x S ) d μ S ( x S ) . \int_{X_S}\left(\prod_{i\in S}f_i(x_i)\right)\left(U_SK^{S,T}U_T\left[\prod_{j\in T}f_j\right]\right)(x_S)\,d\mu_S(x_S). ∫ X S ( i ∈ S ∏ f i ( x i ) ) U S K S , T U T j ∈ T ∏ f j ( x S ) d μ S ( x S ) .
Applying Cauchy–Schwarz gives
∥ K S , T ∥ 2 → 2 ∥ ∏ i ∈ S f i ∥ L 2 ( X S , μ S ) ∥ ∏ j ∈ T f j ∥ L 2 ( X T , μ T ) . \left\|K^{S,T}\right\|_{2\to2}
\left\|\prod_{i\in S}f_i\right\|_{L^2(X_S,\mu_S)}
\left\|\prod_{j\in T}f_j\right\|_{L^2(X_T,\mu_T)}. K S , T 2 → 2 i ∈ S ∏ f i L 2 ( X S , μ S ) j ∈ T ∏ f j L 2 ( X T , μ T ) .
The product measures give
∥ ∏ i ∈ S f i ∥ L 2 ( X S , μ S ) = ∏ i ∈ S ∥ f i ∥ L 2 ( X i , μ i ) , ∥ ∏ j ∈ T f j ∥ L 2 ( X T , μ T ) = ∏ j ∈ T ∥ f j ∥ L 2 ( X j , μ j ) , \left\|\prod_{i\in S}f_i\right\|_{L^2(X_S,\mu_S)}
=\prod_{i\in S}\left\|f_i\right\|_{L^2(X_i,\mu_i)},\qquad
\left\|\prod_{j\in T}f_j\right\|_{L^2(X_T,\mu_T)}
=\prod_{j\in T}\left\|f_j\right\|_{L^2(X_j,\mu_j)}, i ∈ S ∏ f i L 2 ( X S , μ S ) = i ∈ S ∏ ∥ f i ∥ L 2 ( X i , μ i ) , j ∈ T ∏ f j L 2 ( X T , μ T ) = j ∈ T ∏ ∥ f j ∥ L 2 ( X j , μ j ) ,
which proves the claim. ∙უნ
Tensorization
We now consider a cut operator which naturally appears in the present problem. Take X i = [ R ] d X_i=[R]^d X i = [ R ] d with counting measure, and let Θ = ( Θ i j ) i ∈ S , j ∈ T ∈ T ∣ S ∣ ∣ T ∣ \Theta=(\Theta_{ij})_{i\in S,j\in T}\in\mathbb{T}^{|S||T|} Θ = ( Θ ij ) i ∈ S , j ∈ T ∈ T ∣ S ∣∣ T ∣ . For z = ( z 0 , … , z K − 1 ) ∈ ( [ R ] d ) K z=(z_0,\ldots,z_{K-1})\in([R]^d)^K z = ( z 0 , … , z K − 1 ) ∈ ([ R ] d ) K , set
Q S , T ( z S , z T ) : = ∑ i ∈ S , j ∈ T Θ i j z i ⋅ z j . Q^{S,T}(z_S,z_T):=\sum_{i\in S,j\in T}\Theta_{ij}z_i\cdot z_j. Q S , T ( z S , z T ) := i ∈ S , j ∈ T ∑ Θ ij z i ⋅ z j .
The resulting cut operator is
K R , d S , T ( Θ ) : ℓ 2 ( ( [ R ] d ) T ) ⟶ ℓ 2 ( ( [ R ] d ) S ) , \mathcal{K}^{S,T}_{R,d}(\Theta):\ell^2(([R]^d)^T)\longrightarrow\ell^2(([R]^d)^S), K R , d S , T ( Θ ) : ℓ 2 (([ R ] d ) T ) ⟶ ℓ 2 (([ R ] d ) S ) ,
( K R , d S , T ( Θ ) h ) ( z S ) : = ∑ z T ∈ ( [ R ] d ) T e ( ∑ i ∈ S , j ∈ T Θ i j z i ⋅ z j ) h ( z T ) . (\mathcal{K}^{S,T}_{R,d}(\Theta)h)(z_S):=\sum_{z_T\in([R]^d)^T}e\left(\sum_{i\in S,j\in T}\Theta_{ij}z_i\cdot z_j\right)h(z_T). ( K R , d S , T ( Θ ) h ) ( z S ) := z T ∈ ([ R ] d ) T ∑ e i ∈ S , j ∈ T ∑ Θ ij z i ⋅ z j h ( z T ) .
We identify this d d d -dimensional cut operator with an R d ∣ S ∣ × R d ∣ T ∣ R^{d|S|}\times R^{d|T|} R d ∣ S ∣ × R d ∣ T ∣ matrix:
K R , d S , T ( Θ ) = ( e ( ∑ i ∈ S , j ∈ T Θ i j z i ⋅ z j ) ) z S ∈ ( [ R ] d ) S z T ∈ ( [ R ] d ) T . \mathcal{K}^{S,T}_{R,d}(\Theta)=\left(e\left(\sum_{i\in S,j\in T}\Theta_{ij}z_i\cdot z_j\right)\right)_{\substack{z_S\in([R]^d)^S\\z_T\in([R]^d)^T}}. K R , d S , T ( Θ ) = e i ∈ S , j ∈ T ∑ Θ ij z i ⋅ z j z S ∈ ([ R ] d ) S z T ∈ ([ R ] d ) T .
For the one-dimensional case, we write K R S , T ( Θ ) : = K R , 1 S , T ( Θ ) \mathcal{K}^{S,T}_{R}(\Theta):=\mathcal{K}^{S,T}_{R,1}(\Theta) K R S , T ( Θ ) := K R , 1 S , T ( Θ ) and define the normalized cut norm by
m R S , T ( Θ ) : = R − K / 2 ∥ K R S , T ( Θ ) ∥ 2 → 2 . (35) m^{S,T}_{R}(\Theta):=R^{-K/2}\|\mathcal{K}^{S,T}_{R}(\Theta)\|_{2\to2}. \tag*{(35)} m R S , T ( Θ ) := R − K /2 ∥ K R S , T ( Θ ) ∥ 2 → 2 . (35)
By Cauchy–Schwarz, the operator norm of a finite matrix is at most its Hilbert–Schmidt norm. Since the R K R^K R K entries of K R S , T ( Θ ) \mathcal{K}^{S,T}_{R}(\Theta) K R S , T ( Θ ) all have modulus one,
∥ K R S , T ( Θ ) ∥ 2 → 2 ≤ ∥ K R S , T ( Θ ) ∥ H S = R K / 2 , \|\mathcal{K}^{S,T}_{R}(\Theta)\|_{2\to2}\leq\|\mathcal{K}^{S,T}_{R}(\Theta)\|_{\mathrm{HS}}=R^{K/2}, ∥ K R S , T ( Θ ) ∥ 2 → 2 ≤ ∥ K R S , T ( Θ ) ∥ HS = R K /2 ,
and therefore 0 ≤ m R S , T ( Θ ) ≤ 1 0\leq m^{S,T}_{R}(\Theta)\leq1 0 ≤ m R S , T ( Θ ) ≤ 1 .
The following lemma gives a relationship between the operator norm of K R S , T \mathcal{K}^{S,T}_{R} K R S , T and K R , d S , T \mathcal{K}^{S,T}_{R,d} K R , d S , T . This reduction plays a key role throughout the proof.
Lemma 5.2. For every cut S ∣ T S\mid T S ∣ T ,
∥ K R , d S , T ( Θ ) ∥ 2 → 2 = ∥ K R S , T ( Θ ) ∥ 2 → 2 d = R K d / 2 m R S , T ( Θ ) d . \|\mathcal{K}^{S,T}_{R,d}(\Theta)\|_{2\to2}=\|\mathcal{K}^{S,T}_{R}(\Theta)\|_{2\to2}^{d}=R^{Kd/2}m^{S,T}_{R}(\Theta)^d. ∥ K R , d S , T ( Θ ) ∥ 2 → 2 = ∥ K R S , T ( Θ ) ∥ 2 → 2 d = R K d /2 m R S , T ( Θ ) d .
Proof. Write z i = ( z i , 1 , … , z i , d ) z_i=(z_{i,1},\ldots,z_{i,d}) z i = ( z i , 1 , … , z i , d ) and put
z S , ℓ : = ( z i , ℓ ) i ∈ S ∈ [ R ] S , z T , ℓ : = ( z j , ℓ ) j ∈ T ∈ [ R ] T ( 1 ≤ ℓ ≤ d ) . z_{S,\ell}:=(z_{i,\ell})_{i\in S}\in[R]^S,\qquad z_{T,\ell}:=(z_{j,\ell})_{j\in T}\in[R]^T\qquad(1\leq\ell\leq d). z S , ℓ := ( z i , ℓ ) i ∈ S ∈ [ R ] S , z T , ℓ := ( z j , ℓ ) j ∈ T ∈ [ R ] T ( 1 ≤ ℓ ≤ d ) .
Thus the row index z S z_S z S is the same data as ( z S , 1 , … , z S , d ) (z_{S,1},\ldots,z_{S,d}) ( z S , 1 , … , z S , d ) , and the column index z T z_T z T is the same data as ( z T , 1 , … , z T , d ) (z_{T,1},\ldots,z_{T,d}) ( z T , 1 , … , z T , d ) . Under this identification, the ( z S , z T ) (z_S,z_T) ( z S , z T ) -entry of K R , d S , T ( Θ ) \mathcal{K}^{S,T}_{R,d}(\Theta) K R , d S , T ( Θ ) is
e ( ∑ i ∈ S , j ∈ T Θ i j z i ⋅ z j ) = e ( ∑ ℓ = 1 d ∑ i ∈ S , j ∈ T Θ i j z i , ℓ z j , ℓ ) = ∏ ℓ = 1 d e ( ∑ i ∈ S , j ∈ T Θ i j z i , ℓ z j , ℓ ) . \begin{aligned}
e\left(\sum_{i\in S,j\in T}\Theta_{ij}z_i\cdot z_j\right)=e\left(\sum_{\ell=1}^{d}\sum_{i\in S,j\in T}\Theta_{ij}z_{i,\ell}z_{j,\ell}\right) \\
&=\prod_{\ell=1}^{d}e\left(\sum_{i\in S,j\in T}\Theta_{ij}z_{i,\ell}z_{j,\ell}\right).
\end{aligned} e i ∈ S , j ∈ T ∑ Θ ij z i ⋅ z j = e ℓ = 1 ∑ d i ∈ S , j ∈ T ∑ Θ ij z i , ℓ z j , ℓ = ℓ = 1 ∏ d e i ∈ S , j ∈ T ∑ Θ ij z i , ℓ z j , ℓ .
For each ℓ \ell ℓ , the corresponding factor is the ( z S , ℓ , z T , ℓ ) (z_{S,\ell},z_{T,\ell}) ( z S , ℓ , z T , ℓ ) -entry of K R S , T ( Θ ) \mathcal{K}^{S,T}_{R}(\Theta) K R S , T ( Θ ) . This is precisely the definition of a d d d -fold tensor product:
K R , d S , T ( Θ ) = ( K R S , T ( Θ ) ) ⊗ d . \mathcal{K}^{S,T}_{R,d}(\Theta)=\left(\mathcal{K}^{S,T}_{R}(\Theta)\right)^{\otimes d}. K R , d S , T ( Θ ) = ( K R S , T ( Θ ) ) ⊗ d .
The standard multiplicativity of the 2 → 2 2 \to2 2 → 2 operator norm under tensor products now gives
∥ K R , d S , T ( Θ ) ∥ 2 → 2 = ∥ K R S , T ( Θ ) ∥ 2 → 2 d . \left\|\mathcal{K}^{S,T}_{R,d}(\Theta)\right\|_{2\to2}=\left\|\mathcal{K}^{S,T}_{R}(\Theta)\right\|_{2\to2}^{d}. K R , d S , T ( Θ ) 2 → 2 = K R S , T ( Θ ) 2 → 2 d .
Finally, by the definition of m R S , T ( Θ ) m^{S,T}_{R}(\Theta) m R S , T ( Θ ) ,
∥ K R S , T ( Θ ) ∥ 2 → 2 d = R K d / 2 m R S , T ( Θ ) d . \left\|\mathcal{K}^{S,T}_{R}(\Theta)\right\|_{2\to2}^{d}=R^{Kd/2}m^{S,T}_{R}(\Theta)^{d}. K R S , T ( Θ ) 2 → 2 d = R K d /2 m R S , T ( Θ ) d .
The cut operators K R S , T \mathcal{K}^{S,T}_{R} K R S , T appear in both the major and minor arc estimates. On the minor arcs, Lemma 5.1 and Lemma 5.2 reduce the analysis to understanding m N S , T ( − 2 β S , T ) m^{S,T}_{N}(-2\beta_{S,T}) m N S , T ( − 2 β S , T ) . On the major arcs, the same reductions lead us to m N S , T ( − 2 θ S , T ) m^{S,T}_{N}(-2\theta_{S,T}) m N S , T ( − 2 θ S , T ) paired with the complete-sum factor ∣ im ( 2 a S , T ) ∣ − d / 2 \lvert\operatorname{im}(2a_{S,T})\rvert^{-d/2} ∣ im ( 2 a S , T ) ∣ − d /2 . Here we used the following useful notation: for the coefficient vector b = ( b e ) e ∈ E b=(b_e)_{e\in\mathcal{E}} b = ( b e ) e ∈ E , put b i j = b j i : = b { i , j } b_{ij}=b_{ji}:=b_{\{i,j\}} b ij = b j i := b { i , j } and write b S , T : = ( b i j ) i ∈ S , j ∈ T b_{S,T}:=(b_{ij})_{i\in S,j\in T} b S , T := ( b ij ) i ∈ S , j ∈ T for its matrix of crossing coefficients.
Reduction to one crossing edge
We next reduce the normalized cut norm to the single-edge norm associated with any one crossing edge. For θ ∈ T \theta\in\mathbb{T} θ ∈ T , define the single-edge matrix and its normalized norm by
K R ( θ ) : = ( e ( θ r s ) ) r , s ∈ [ R ] , m R ( θ ) : = R − 1 ∥ K R ( θ ) ∥ 2 → 2 . \mathcal{K}_{R}(\theta):=\left(e(\theta rs)\right)_{r,s\in[R]},\qquad m_{R}(\theta):=R^{-1}\left\|\mathcal{K}_{R}(\theta)\right\|_{2\to2}. K R ( θ ) := ( e ( θ r s ) ) r , s ∈ [ R ] , m R ( θ ) := R − 1 ∥ K R ( θ ) ∥ 2 → 2 .
This is the cut operator associated with a cut having one vertex on each side. Thus K R ( θ ) \mathcal{K}_{R}(\theta) K R ( θ ) is an R × R R\times R R × R matrix acting on ℓ 2 ( [ R ] ) \ell^{2}([R]) ℓ 2 ([ R ]) , and its r r r th output coordinate is ∑ s ∈ [ R ] e ( θ r s ) h ( s ) \sum_{s\in[R]}e(\theta rs)h(s) ∑ s ∈ [ R ] e ( θ r s ) h ( s ) . Its Hilbert–Schmidt norm is R R R , so 0 ≤ m R ( θ ) ≤ 1 0\le m_{R}(\theta)\le1 0 ≤ m R ( θ ) ≤ 1 , and complex conjugation gives m R ( − θ ) = m R ( θ ) m_{R}(-\theta)=m_{R}(\theta) m R ( − θ ) = m R ( θ ) .
We shall use the standard block form of Schur’s test: if M = ( M a b ) M=(M_{ab}) M = ( M ab ) is a p × q p\times q p × q matrix of operators, then
∥ M ∥ 2 → 2 ≤ p q max a , b ∥ M a b ∥ 2 → 2 . (36) \|M\|_{2\to2}\le\sqrt{pq}\max_{a,b}\|M_{ab}\|_{2\to2}. \tag*{(36)} ∥ M ∥ 2 → 2 ≤ pq a , b max ∥ M ab ∥ 2 → 2 . (36)
Lemma 5.3 (Single-edge bound). Let Θ ∈ T ∣ S ∣ ∣ T ∣ \Theta\in\mathbb{T}^{|S||T|} Θ ∈ T ∣ S ∣∣ T ∣ and let i ∈ S i\in S i ∈ S , j ∈ T j\in T j ∈ T . Then m R S , T ( Θ ) ≤ m R ( Θ i j ) m^{S,T}_{R}(\Theta)\le m_{R}(\Theta_{ij}) m R S , T ( Θ ) ≤ m R ( Θ ij ) .
Proof. Abbreviate K : = K R S , T ( Θ ) \mathcal{K}:=\mathcal{K}^{S,T}_{R}(\Theta) K := K R S , T ( Θ ) and separate the two coordinates belonging to the selected edge:
z ^ S : = ( z k ) k ∈ S ∖ { i } , z ^ T : = ( z ℓ ) ℓ ∈ T ∖ { j } . \widehat{z}_{S}:=(z_k)_{k\in S\setminus\{i\}},\qquad\widehat{z}_{T}:=(z_\ell)_{\ell\in T\setminus\{j\}}. z S := ( z k ) k ∈ S ∖ { i } , z T := ( z ℓ ) ℓ ∈ T ∖ { j } .
For fixed z ^ S \widehat{z}_{S} z S and z ^ T \widehat{z}_{T} z T , define
K [ z ^ S , z ^ T ] : = ( e ( Q S , T ( z S , z T ) ) ) z i , z j ∈ [ R ] , \mathcal{K}[\widehat{z}_{S},\widehat{z}_{T}]:=\left(e\left(Q^{S,T}(z_{S},z_{T})\right)\right)_{z_i,z_j\in[R]}, K [ z S , z T ] := ( e ( Q S , T ( z S , z T ) ) ) z i , z j ∈ [ R ] ,
where the coordinates contained in z ^ S \widehat{z}_{S} z S and z ^ T \widehat{z}_{T} z T are held fixed. This is the R × R R\times R R × R block of K \mathcal{K} K in which z i z_i z i indexes the rows and z j z_j z j indexes the columns.
Every row is specified by ( z ^ S , z i ) (\widehat{z}_{S},z_i) ( z S , z i ) and every column by ( z ^ T , z j ) (\widehat{z}_{T},z_j) ( z T , z j ) . Grouping them by the hatted tuples therefore gives the block matrix
K = ( K [ z ^ S , z ^ T ] ) z ^ S ∈ [ R ] S ∖ { i } z ^ T ∈ [ R ] T ∖ { j } . \mathcal{K}=\left(\mathcal{K}[\widehat{z}_{S},\widehat{z}_{T}]\right)_{\substack{\widehat{z}_{S}\in[R]^{S\setminus\{i\}}\\ \widehat{z}_{T}\in[R]^{T\setminus\{j\}}}}. K = ( K [ z S , z T ] ) z S ∈ [ R ] S ∖ { i } z T ∈ [ R ] T ∖ { j } .
Thus there are R ∣ S ∣ − 1 R^{|S|-1} R ∣ S ∣ − 1 block rows and R ∣ T ∣ − 1 R^{|T|-1} R ∣ T ∣ − 1 block columns, and every block is R × R R \times R R × R . The definition of Q S , T Q^{S,T} Q S , T gives
Q S , T ( z S , z T ) = Θ i j z i z j + z i ∑ ℓ ∈ T ∖ { j } Θ i ℓ z ℓ + z j ∑ k ∈ S ∖ { i } Θ k j z k + ∑ k ∈ S ∖ { i } ℓ ∈ T ∖ { j } Θ k ℓ z k z ℓ . (37) Q^{S,T}(z_S,z_T)=\Theta_{ij}z_i z_j+z_i\sum_{\ell\in T\setminus\{j\}}\Theta_{i\ell}z_\ell+z_j\sum_{k\in S\setminus\{i\}}\Theta_{kj}z_k+\sum_{\substack{k\in S\setminus\{i\}\\ \ell\in T\setminus\{j\}}}\Theta_{k\ell}z_kz_\ell. \tag*{(37)} Q S , T ( z S , z T ) = Θ ij z i z j + z i ℓ ∈ T ∖ { j } ∑ Θ i ℓ z ℓ + z j k ∈ S ∖ { i } ∑ Θ k j z k + k ∈ S ∖ { i } ℓ ∈ T ∖ { j } ∑ Θ k ℓ z k z ℓ . (37)
For t ∈ T t\in T t ∈ T , set
D R ( t ) : = ( e ( t ) 0 ⋮ ⋱ 0 e ( R t ) ) . D_R(t):=\begin{pmatrix} e(t) & 0 \\ \vdots& \ddots\\ 0 & e(Rt) \end{pmatrix}. D R ( t ) := e ( t ) ⋮ 0 0 ⋱ e ( R t ) .
Consequently the block has the factorization
K [ z ~ S , z ~ T ] = e ( ∑ k ∈ S ∖ { i } ℓ ∈ T ∖ { j } Θ k ℓ z k z ℓ ) D R ( ∑ ℓ ∈ T ∖ { j } Θ i ℓ z ℓ ) K R ( Θ i j ) D R ( ∑ k ∈ S ∖ { i } Θ k j z k ) . \mathcal{K}[\widetilde z_S,\widetilde z_T]
=e\left(\sum_{\substack{k\in S\setminus\{i\}\\ \ell\in T\setminus\{j\}}}\Theta_{k\ell}z_kz_\ell\right)
D_R\left(\sum_{\ell\in T\setminus\{j\}}\Theta_{i\ell}z_\ell\right)
\mathcal{K}_R(\Theta_{ij})
D_R\left(\sum_{k\in S\setminus\{i\}}\Theta_{kj}z_k\right). K [ z S , z T ] = e k ∈ S ∖ { i } ℓ ∈ T ∖ { j } ∑ Θ k ℓ z k z ℓ D R ℓ ∈ T ∖ { j } ∑ Θ i ℓ z ℓ K R ( Θ ij ) D R k ∈ S ∖ { i } ∑ Θ k j z k .
The scalar factor supplies the last term in (37) . The left and right diagonal factors supply the terms linear in z i z_i z i and z j z_j z j , respectively, while K R ( Θ i j ) \mathcal{K}_R(\Theta_{ij}) K R ( Θ ij ) supplies the term Θ i j z i z j \Theta_{ij}z_i z_j Θ ij z i z j . The scalar has modulus one and the two diagonal matrices are unitary, so every block satisfies
∥ K [ z ~ S , z ~ T ] ∥ 2 → 2 = ∥ K R ( Θ i j ) ∥ 2 → 2 . \left\|\mathcal{K}[\widetilde z_S,\widetilde z_T]\right\|_{2\to2}
=\left\|\mathcal{K}_R(\Theta_{ij})\right\|_{2\to2}. ∥ K [ z S , z T ] ∥ 2 → 2 = ∥ K R ( Θ ij ) ∥ 2 → 2 .
Applying (36) to the R ∣ S ∣ − 1 × R ∣ T ∣ − 1 R^{|S|-1}\times R^{|T|-1} R ∣ S ∣ − 1 × R ∣ T ∣ − 1 block matrix yields
∥ K ∥ 2 → 2 ≤ R ( ∣ S ∣ − 1 ) / 2 R ( ∣ T ∣ − 1 ) / 2 ∥ K R ( Θ i j ) ∥ 2 → 2 = R ( K − 2 ) / 2 ∥ K R ( Θ i j ) ∥ 2 → 2 . \|\mathcal{K}\|_{2\to2}\le R^{(|S|-1)/2}R^{(|T|-1)/2}\|\mathcal{K}_R(\Theta_{ij})\|_{2\to2}
=R^{(K-2)/2}\|\mathcal{K}_R(\Theta_{ij})\|_{2\to2}. ∥ K ∥ 2 → 2 ≤ R ( ∣ S ∣ − 1 ) /2 R ( ∣ T ∣ − 1 ) /2 ∥ K R ( Θ ij ) ∥ 2 → 2 = R ( K − 2 ) /2 ∥ K R ( Θ ij ) ∥ 2 → 2 .
Using the two normalizations now gives
m R S , T ( Θ ) = R − K / 2 ∥ K ∥ 2 → 2 ≤ R − 1 ∥ K R ( Θ i j ) ∥ 2 → 2 = m R ( Θ i j ) . m_R^{S,T}(\Theta)=R^{-K/2}\|\mathcal{K}\|_{2\to2}\le R^{-1}\|\mathcal{K}_R(\Theta_{ij})\|_{2\to2}=m_R(\Theta_{ij}). m R S , T ( Θ ) = R − K /2 ∥ K ∥ 2 → 2 ≤ R − 1 ∥ K R ( Θ ij ) ∥ 2 → 2 = m R ( Θ ij ) .
A high-moment bound
We now prove the one-cut mean-value estimate stated in Lemma 2.1. Its arithmetic input is the matrix count from Appendix B, which is derived from Kim’s mean-value formula for primitive lattice flags.
Proof of Lemma 2.1. Abbreviate K : = K R S , T ( Θ ) \mathcal{K}:=\mathcal{K}_R^{S,T}(\Theta) K := K R S , T ( Θ ) . The matrix K K ∗ \mathcal{K}\mathcal{K}^* K K ∗ is Hermitian and positive semidefinite. If its eigenvalues are λ 1 , … , λ R ∣ S ∣ ≥ 0 \lambda_1,\ldots,\lambda_{R^{|S|}}\ge0 λ 1 , … , λ R ∣ S ∣ ≥ 0 , then
∥ K ∥ 2 → 2 2 K + 4 = ( max a λ a ) K + 2 ≤ ∑ a λ a K + 2 = tr ( ( K K ∗ ) K + 2 ) . (38) \|\mathcal{K}\|_{2\to2}^{2K+4}
=\left(\max_a\lambda_a\right)^{K+2}
\le\sum_a\lambda_a^{K+2}
=\operatorname{tr}\left((\mathcal{K}\mathcal{K}^*)^{K+2}\right). \tag*{(38)} ∥ K ∥ 2 → 2 2 K + 4 = ( a max λ a ) K + 2 ≤ a ∑ λ a K + 2 = tr ( ( K K ∗ ) K + 2 ) . (38)
In this way we reduce the proof to analyzing tr ( ( K K ∗ ) K + 2 ) \operatorname{tr}\left((\mathcal{K}\mathcal{K}^*)^{K+2}\right) tr ( ( K K ∗ ) K + 2 ) .
For z S , z ~ S ∈ [ R ] S z_S,\widetilde z_S\in[R]^S z S , z S ∈ [ R ] S , ordinary matrix multiplication gives the ( z S , z ~ S ) (z_S,\widetilde z_S) ( z S , z S ) -entry of K K ∗ \mathcal{K}\mathcal{K}^* K K ∗ as
( K K ∗ ) z S , z ~ S = ∑ z T ∈ [ R ] T e ( ∑ i ∈ S , j ∈ T Θ i j z i z j ) e ( ∑ i ∈ S , j ∈ T Θ i j z ~ i z j ) ‾ = ∑ z T ∈ [ R ] T e ( ∑ i ∈ S , j ∈ T Θ i j ( z i − z ~ i ) z j ) . (39) (\mathcal{K}\mathcal{K}^*)_{z_S,\widetilde z_S}
=\sum_{z_T\in[R]^T}e\left(\sum_{\substack{i\in S,j\in T}}\Theta_{ij}z_i z_j\right)
\overline{e\left(\sum_{\substack{i\in S,j\in T}}\Theta_{ij}\widetilde z_i z_j\right)}
=\sum_{z_T\in[R]^T}e\left(\sum_{\substack{i\in S,j\in T}}\Theta_{ij}(z_i-\widetilde z_i)z_j\right). \tag*{(39)} ( K K ∗ ) z S , z S = z T ∈ [ R ] T ∑ e i ∈ S , j ∈ T ∑ Θ ij z i z j e i ∈ S , j ∈ T ∑ Θ ij z i z j = z T ∈ [ R ] T ∑ e i ∈ S , j ∈ T ∑ Θ ij ( z i − z i ) z j . (39)
and expanding the trace gives
tr ( ( K K ∗ ) K + 2 ) = ∑ z S ( 1 ) , … , z S ( K + 2 ) ∈ [ R ] S ( K K ∗ ) z S ( 1 ) , z S ( 2 ) ⋯ ( K K ∗ ) z S ( K + 2 ) , z S ( 1 ) . (40) \operatorname{tr}\left((\mathcal{K}\mathcal{K}^{*})^{K+2}\right)=\sum_{z_S^{(1)},\ldots,z_S^{(K+2)}\in[R]^S}(\mathcal{K}\mathcal{K}^{*})_{z_S^{(1)},z_S^{(2)}}\cdots(\mathcal{K}\mathcal{K}^{*})_{z_S^{(K+2)},z_S^{(1)}}. \tag*{(40)} tr ( ( K K ∗ ) K + 2 ) = z S ( 1 ) , … , z S ( K + 2 ) ∈ [ R ] S ∑ ( K K ∗ ) z S ( 1 ) , z S ( 2 ) ⋯ ( K K ∗ ) z S ( K + 2 ) , z S ( 1 ) . (40)
Combining (39) with (40) yields
tr ( ( K K ∗ ) K + 2 ) = ∑ z S ( 1 ) , … , z S ( K + 2 ) ∈ [ R ] S ∑ z T ( 1 ) , … , z T ( K + 2 ) ∈ [ R ] T e ( ∑ i ∈ S , j ∈ T Θ i j [ ∑ h = 1 K + 1 ( z i ( h ) − z i ( h + 1 ) ) z j ( h ) + ( z i ( K + 2 ) − z i ( 1 ) ) z j ( K + 2 ) ] ) . \begin{aligned}
\operatorname{tr}\left((\mathcal{K}\mathcal{K}^{*})^{K+2}\right)
={}&\sum_{\substack{z_S^{(1)},\ldots,z_S^{(K+2)}\\ \in[R]^S}}\sum_{\substack{z_T^{(1)},\ldots,z_T^{(K+2)}\\ \in[R]^T}}e\left(\sum_{i\in S,j\in T}\Theta_{ij}\left[\sum_{h=1}^{K+1}\left(z_i^{(h)}-z_i^{(h+1)}\right)z_j^{(h)}+\left(z_i^{(K+2)}-z_i^{(1)}\right)z_j^{(K+2)}\right]\right).
\end{aligned} tr ( ( K K ∗ ) K + 2 ) = z S ( 1 ) , … , z S ( K + 2 ) ∈ [ R ] S ∑ z T ( 1 ) , … , z T ( K + 2 ) ∈ [ R ] T ∑ e i ∈ S , j ∈ T ∑ Θ ij [ h = 1 ∑ K + 1 ( z i ( h ) − z i ( h + 1 ) ) z j ( h ) + ( z i ( K + 2 ) − z i ( 1 ) ) z j ( K + 2 ) ] .
Now integrate each Θ i j \Theta_{ij} Θ ij over T T T and apply the orthogonality relation. The equations imposed by orthogonality are precisely the entries of the matrix equation
∑ h = 1 K + 1 ( z S ( h ) − z S ( h + 1 ) ) ( z T ( h ) ) T + ( z S ( K + 2 ) − z S ( 1 ) ) ( z T ( K + 2 ) ) T = 0. (41) \sum_{h=1}^{K+1}\left(z_S^{(h)}-z_S^{(h+1)}\right)\left(z_T^{(h)}\right)^{\mathsf{T}}+\left(z_S^{(K+2)}-z_S^{(1)}\right)\left(z_T^{(K+2)}\right)^{\mathsf{T}}=0. \tag*{(41)} h = 1 ∑ K + 1 ( z S ( h ) − z S ( h + 1 ) ) ( z T ( h ) ) T + ( z S ( K + 2 ) − z S ( 1 ) ) ( z T ( K + 2 ) ) T = 0. (41)
Consequently, the integral of the trace is the number of choices z S ( h ) ∈ [ R ] S z_S^{(h)}\in[R]^S z S ( h ) ∈ [ R ] S and z T ( h ) ∈ [ R ] T z_T^{(h)}\in[R]^T z T ( h ) ∈ [ R ] T , 1 ≤ h ≤ K + 2 1\le h\le K+2 1 ≤ h ≤ K + 2 , satisfying this matrix equation.
We next put this constraint into the form required by Lemma B.1. The differences on the S S S -side telescope:
( z S ( 1 ) − z S ( 2 ) ) + ⋯ + ( z S ( K + 2 ) − z S ( 1 ) ) = 0. \left(z_S^{(1)}-z_S^{(2)}\right)+\cdots+\left(z_S^{(K+2)}-z_S^{(1)}\right)=0. ( z S ( 1 ) − z S ( 2 ) ) + ⋯ + ( z S ( K + 2 ) − z S ( 1 ) ) = 0.
We may therefore subtract z T ( K + 2 ) z_T^{(K+2)} z T ( K + 2 ) from every T T T -variable in (41) : the change in its left-hand side is the outer product of the zero vector above with ( z T ( K + 2 ) ) \left(z_T^{(K+2)}\right) ( z T ( K + 2 ) ) . The final term then vanishes, and the matrix equation becomes
∑ h = 1 K + 1 ( z S ( h ) − z S ( h + 1 ) ) ( z T ( h ) − z T ( K + 2 ) ) T = 0. \sum_{h=1}^{K+1}\left(z_S^{(h)}-z_S^{(h+1)}\right)\left(z_T^{(h)}-z_T^{(K+2)}\right)^{\mathsf{T}}=0. h = 1 ∑ K + 1 ( z S ( h ) − z S ( h + 1 ) ) ( z T ( h ) − z T ( K + 2 ) ) T = 0.
For 1 ≤ h ≤ K + 1 1\le h\le K+1 1 ≤ h ≤ K + 1 , set
U h : = z S ( h ) − z S ( h + 1 ) ∈ Z S , V h : = z T ( h ) − z T ( K + 2 ) ∈ Z T , U_h:=z_S^{(h)}-z_S^{(h+1)}\in\mathbb{Z}^{S},\qquad V_h:=z_T^{(h)}-z_T^{(K+2)}\in\mathbb{Z}^{T}, U h := z S ( h ) − z S ( h + 1 ) ∈ Z S , V h := z T ( h ) − z T ( K + 2 ) ∈ Z T ,
and place these vectors in the columns of
U = ( U 1 ⋯ U K + 1 ) ∈ Z ∣ S ∣ × ( K + 1 ) , V = ( V 1 ⋯ V K + 1 ) ∈ Z ∣ T ∣ × ( K + 1 ) . U=(U_1\ \cdots\ U_{K+1})\in\mathbb{Z}^{|S|\times(K+1)},\qquad V=(V_1\ \cdots\ V_{K+1})\in\mathbb{Z}^{|T|\times(K+1)}. U = ( U 1 ⋯ U K + 1 ) ∈ Z ∣ S ∣ × ( K + 1 ) , V = ( V 1 ⋯ V K + 1 ) ∈ Z ∣ T ∣ × ( K + 1 ) .
Since U V T = ∑ h = 1 K + 1 U h V h T UV^{\mathsf{T}}=\sum_{h=1}^{K+1}U_hV_h^{\mathsf{T}} U V T = ∑ h = 1 K + 1 U h V h T , equation (41) is precisely U V T = 0 UV^{\mathsf{T}}=0 U V T = 0 .
Once U U U and V V V are fixed, the variables
z S ( 1 ) , … , z S ( K + 2 ) and z T ( 1 ) , … , z T ( K + 2 ) z_S^{(1)},\ldots,z_S^{(K+2)}\qquad\text{and}\qquad z_T^{(1)},\ldots,z_T^{(K+2)} z S ( 1 ) , … , z S ( K + 2 ) and z T ( 1 ) , … , z T ( K + 2 )
are determined by the two base points z S ( 1 ) ∈ [ R ] S z_S^{(1)}\in[R]^S z S ( 1 ) ∈ [ R ] S and z T ( K + 2 ) ∈ [ R ] T z_T^{(K+2)}\in[R]^T z T ( K + 2 ) ∈ [ R ] T . Indeed,
z S ( h + 1 ) = z S ( h ) − U h , z T ( h ) = z T ( K + 2 ) + V h ( 1 ≤ h ≤ K + 1 ) . z_S^{(h+1)}=z_S^{(h)}-U_h,\qquad z_T^{(h)}=z_T^{(K+2)}+V_h\qquad(1\le h\le K+1). z S ( h + 1 ) = z S ( h ) − U h , z T ( h ) = z T ( K + 2 ) + V h ( 1 ≤ h ≤ K + 1 ) .
Thus each pair U , V U,V U , V arises from at most R ∣ S ∣ R ∣ T ∣ = R K R^{|S|}R^{|T|}=R^K R ∣ S ∣ R ∣ T ∣ = R K choices of these variables. Some choices of the two base points may reconstruct variables outside [ R ] S [R]^S [ R ] S or [ R ] T [R]^T [ R ] T , which only decreases this number.
All entries of U U U and V V V lie in [ − R , R ] [-R,R] [ − R , R ] . Moreover, balancedness gives 2 max ( ∣ S ∣ , ∣ T ∣ ) ≤ K + 1 2\max(|S|,|T|) \le K+1 2 max ( ∣ S ∣ , ∣ T ∣ ) ≤ K + 1 , while ∣ S ∣ + ∣ T ∣ = K < K + 1 |S|+|T|=K<K+1 ∣ S ∣ + ∣ T ∣ = K < K + 1 . Thus Lemma B.1 applies with n = K + 1 n=K+1 n = K + 1 and shows that the number of possible pairs U , V U,V U , V is
≪ K R ( K + 1 ) ( ∣ S ∣ + ∣ T ∣ ) − 2 ∣ S ∣ ∣ T ∣ = R K ( K + 1 ) − 2 ∣ S ∣ ∣ T ∣ . \ll_K R^{(K+1)(|S|+|T|)-2|S||T|}=R^{K(K+1)-2|S||T|}. ≪ K R ( K + 1 ) ( ∣ S ∣ + ∣ T ∣ ) − 2∣ S ∣∣ T ∣ = R K ( K + 1 ) − 2∣ S ∣∣ T ∣ .
Combining this with the R K R^K R K choices of base points gives
∫ T ∣ S ∣ ∣ T ∣ tr ( ( K K ∗ ) K + 2 ) d Θ ≪ K R K ( K + 2 ) − 2 ∣ S ∣ ∣ T ∣ . (42) \int_{\mathbb{T}^{|S||T|}} \operatorname{tr}\left((\mathcal{K}\mathcal{K}^*)^{K+2}\right)\,\mathrm{d}\Theta\ll_K R^{K(K+2)-2|S||T|}. \tag*{(42)} ∫ T ∣ S ∣∣ T ∣ tr ( ( K K ∗ ) K + 2 ) d Θ ≪ K R K ( K + 2 ) − 2∣ S ∣∣ T ∣ . (42)
Finally, (38) yields
m R S , T ( Θ ) 2 K + 4 = R − K ( K + 2 ) ∥ K ∥ 2 → 2 2 K + 4 ≤ R − K ( K + 2 ) tr ( ( K K ∗ ) K + 2 ) . m_R^{S,T}(\Theta)^{2K+4}=R^{-K(K+2)}\|\mathcal{K}\|_{2\to2}^{2K+4}\le R^{-K(K+2)}\operatorname{tr}\left((\mathcal{K}\mathcal{K}^*)^{K+2}\right). m R S , T ( Θ ) 2 K + 4 = R − K ( K + 2 ) ∥ K ∥ 2 → 2 2 K + 4 ≤ R − K ( K + 2 ) tr ( ( K K ∗ ) K + 2 ) .
Integrating this inequality and applying (42) gives
∫ T ∣ S ∣ ∣ T ∣ m R S , T ( Θ ) 2 K + 4 d Θ ≪ K R − 2 ∣ S ∣ ∣ T ∣ , \int_{\mathbb{T}^{|S||T|}}m_R^{S,T}(\Theta)^{2K+4}\,\mathrm{d}\Theta\ll_K R^{-2|S||T|}, ∫ T ∣ S ∣∣ T ∣ m R S , T ( Θ ) 2 K + 4 d Θ ≪ K R − 2∣ S ∣∣ T ∣ ,
which is (12) .
Balanced cuts and fractional Hölder
Recall that a cut S ∣ T S\mid T S ∣ T is balanced if
S ⊔ T = { 0 , … , K − 1 } , ∣ ∣ S ∣ − ∣ T ∣ ∣ ≤ 1. S\mathbin{\sqcup}T=\{0,\ldots,K-1\},\qquad\bigl||S|-|T|\bigr|\le1. S ⊔ T = { 0 , … , K − 1 } , ∣ S ∣ − ∣ T ∣ ≤ 1.
Let B K \mathcal{B}_K B K denote the set of all balanced cuts, with S ∣ T S\mid T S ∣ T and T ∣ S T\mid S T ∣ S regarded as the same cut. Give every cut in B K \mathcal{B}_K B K the common weight w K : = L / ( ⌊ K 2 / 4 ⌋ ∣ B K ∣ ) w_K:=L/(\lfloor K^2/4\rfloor|\mathcal{B}_K|) w K := L / (⌊ K 2 /4 ⌋ ∣ B K ∣ ) .
For a cut S ∣ T S\mid T S ∣ T , regard the edges crossing S ∣ T S\mid T S ∣ T as a subset of E \mathcal{E} E . A fractional cover of E \mathcal{E} E by such sets assigns a nonnegative weight to each cut so that the total weight of the cuts crossed by any fixed edge is at least one. The following lemma shows that, for the weights above, this total is exactly one for every edge.
Lemma 5.4. For every edge e ∈ E e\in\mathcal{E} e ∈ E ,
w K # { S ∣ T ∈ B K : e crosses S ∣ T } = 1. (43) w_K\#\{S\mid T\in\mathcal{B}_K:e\text{ crosses }S\mid T\}=1. \tag*{(43)} w K # { S ∣ T ∈ B K : e crosses S ∣ T } = 1. (43)
Proof. Permuting the vertices preserves B K \mathcal{B}_K B K and acts transitively on the edges of E \mathcal{E} E . Hence every edge crosses the same number of balanced cuts.
Count pairs consisting of a balanced cut and one of its crossing edges. Choosing the cut first gives ∣ B K ∣ ⌊ K 2 / 4 ⌋ |\mathcal{B}_K|\lfloor K^2/4\rfloor ∣ B K ∣ ⌊ K 2 /4 ⌋ pairs, since every balanced cut has ⌊ K 2 / 4 ⌋ \lfloor K^2/4\rfloor ⌊ K 2 /4 ⌋ crossing edges. Choosing the edge first gives
L # { S ∣ T ∈ B K : e crosses S ∣ T } L\#\{S\mid T\in\mathcal{B}_K:e\text{ crosses }S\mid T\} L # { S ∣ T ∈ B K : e crosses S ∣ T }
pairs, since there are L L L edges and the number of balanced cuts crossed by an edge is independent of the edge. Therefore
L # { S ∣ T ∈ B K : e crosses S ∣ T } = ∣ B K ∣ ⌊ K 2 / 4 ⌋ . L\#\{S\mid T\in\mathcal{B}_K:e\text{ crosses }S\mid T\}=|\mathcal{B}_K|\lfloor K^2/4\rfloor. L # { S ∣ T ∈ B K : e crosses S ∣ T } = ∣ B K ∣ ⌊ K 2 /4 ⌋ .
Multiplying by w K / L w_K/L w K / L proves (43) .
For K = 4 K=4 K = 4 , the cover can be read directly from the three diagrams below. The orange and blue vertices lie on the two sides of each cut. Every edge is internal in exactly one panel and crosses the cut in the other two. Since w 4 = 1 / 2 w_4=1/2 w 4 = 1/2 , every edge therefore has total crossing weight one.
Figure 1.
The fractional cover identity (43) is exactly the hypothesis in the following special case of Finner’s fractional form of Hölder’s inequality [4 ] .
Lemma 5.5 (Fractional Hölder inequality). Let E \mathcal{E} E be a finite set. For each e ∈ E e\in\mathcal{E} e ∈ E , let ( X e , μ e ) (X_e,\mu_e) ( X e , μ e ) be a σ \sigma σ -finite measure space, and let μ : = ⨂ e ∈ E μ e \mu:=\bigotimes_{e\in\mathcal{E}}\mu_e μ := ⨂ e ∈ E μ e be the product measure. Let J J J be a positive integer. For each 1 ≤ j ≤ J 1\leq j\leq J 1 ≤ j ≤ J , choose a nonempty subset E j ⊆ E \mathcal{E}_j\subseteq\mathcal{E} E j ⊆ E , a weight c j > 0 c_j>0 c j > 0 , and a nonnegative integrable function f j : ∏ e ∈ E j X e ⟶ [ 0 , ∞ ) f_j:\prod_{e\in\mathcal{E}_j}X_e\longrightarrow[0,\infty) f j : ∏ e ∈ E j X e ⟶ [ 0 , ∞ ) . Denote the product measure on the domain of f j f_j f j by μ j : = ⨂ e ∈ E j μ e \mu_j:=\bigotimes_{e\in\mathcal{E}_j}\mu_e μ j := ⨂ e ∈ E j μ e . Suppose that for every e ∈ E e\in\mathcal{E} e ∈ E , we have
∑ 1 ≤ j ≤ J e ∈ E j c j = 1. \sum_{\substack{1\leq j\leq J\\e\in\mathcal{E}_j}}c_j=1. 1 ≤ j ≤ J e ∈ E j ∑ c j = 1.
Then, writing x = ( x e ) e ∈ E x=(x_e)_{e\in\mathcal{E}} x = ( x e ) e ∈ E ,
∫ ∏ j = 1 J f j ( ( x e ) e ∈ E j ) c j d μ ( x ) ≤ ∏ j = 1 J ( ∫ f j d μ j ) c j . \int\prod_{j=1}^{J}f_j\bigl((x_e)_{e\in\mathcal{E}_j}\bigr)^{c_j}\,\mathrm{d}\mu(x)
\leq
\prod_{j=1}^{J}\left(\int f_j\,\mathrm{d}\mu_j\right)^{c_j}. ∫ j = 1 ∏ J f j ( ( x e ) e ∈ E j ) c j d μ ( x ) ≤ j = 1 ∏ J ( ∫ f j d μ j ) c j .
This is [4 ] , Theorem 2.1, applied with exponents 1 / c j 1/c_j 1/ c j to the functions f j c j f_j^{c_j} f j c j .
The minor arcs
We begin the section by decomposing T L \mathbb{T}^{L} T L into major and minor arcs. For 2 ≤ Q ≤ R 2\leq Q\leq R 2 ≤ Q ≤ R , define the one-dimensional major arcs by
M Q ( 1 ) : = ⋃ q ≤ Q a ∈ Z / q Z ( a , q ) = 1 { ξ ∈ T : ∥ ξ − a q ∥ T ≤ Q q R 2 } . (44) \mathfrak{M}^{(1)}_Q:=\bigcup_{\substack{q\leq Q\\a\in\mathbb{Z}/q\mathbb{Z}\\(a,q)=1}}\left\{\xi\in\mathbb{T}:\left\|\xi-\frac{a}{q}\right\|_{\mathbb{T}}\leq\frac{Q}{qR^2}\right\}. \tag*{(44)} M Q ( 1 ) := q ≤ Q a ∈ Z / q Z ( a , q ) = 1 ⋃ { ξ ∈ T : ξ − q a T ≤ q R 2 Q } . (44)
For r ≥ 1 r\geq1 r ≥ 1 , put
M Q ( r ) : = ( M Q ( 1 ) ) r , m Q ( r ) : = T r ∖ M Q ( r ) . \mathfrak{M}^{(r)}_Q:=\left(\mathfrak{M}^{(1)}_Q\right)^r,\qquad
\mathfrak{m}^{(r)}_Q:=\mathbb{T}^r\setminus\mathfrak{M}^{(r)}_Q. M Q ( r ) := ( M Q ( 1 ) ) r , m Q ( r ) := T r ∖ M Q ( r ) .
Since the scalar coefficients arising from the cut operators are − 2 β e -2\beta_e − 2 β e , define
M Q : = { β ∈ T L : − 2 β ∈ M Q ( L ) } , m Q : = T L ∖ M Q . \mathfrak{M}_Q:=\left\{\beta\in\mathbb{T}^{L}:-2\beta\in\mathfrak{M}^{(L)}_Q\right\},\qquad
\mathfrak{m}_Q:=\mathbb{T}^{L}\setminus\mathfrak{M}_Q. M Q := { β ∈ T L : − 2 β ∈ M Q ( L ) } , m Q := T L ∖ M Q .
Thus
m Q = ⋃ e ∈ E { β ∈ T L : − 2 β e ∈ m Q ( 1 ) } . (45) \mathfrak{m}_Q=\bigcup_{e\in\mathcal{E}}\left\{\beta\in\mathbb{T}^{L}:-2\beta_e\in\mathfrak{m}^{(1)}_Q\right\}. \tag*{(45)} m Q = e ∈ E ⋃ { β ∈ T L : − 2 β e ∈ m Q ( 1 ) } . (45)
The goal of the section is to prove the following minor arc estimate.
Proposition 6.1 (minor arc estimate). *Assume 2 ≤ Q ≤ R 2 \le Q \le R 2 ≤ Q ≤ R and d ≥ 4 K + 4 d \ge4K + 4 d ≥ 4 K + 4 . There is γ = γ ( K , d ) > 0 \gamma= \gamma(K,d) > 0 γ = γ ( K , d ) > 0 such that, for all functions F 0 , … , F K − 1 : Z d → C F_0,\ldots,F_{K-1}:\mathbb{Z}^d \to\mathbb{C} F 0 , … , F K − 1 : Z d → C supported on [ N ] d [N]^d [ N ] d ,
∣ m N β ( F 0 , … , F K − 1 ) ∣ ≪ σ , Δ , d Q − γ R K d / 2 − 2 L ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (46) \left|m_N^{\beta}(F_0,\ldots,F_{K-1})\right| \ll_{\sigma,\Delta,d} Q^{-\gamma}R^{Kd/2-2L}\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. \tag*{(46)} m N β ( F 0 , … , F K − 1 ) ≪ σ , Δ , d Q − γ R K d /2 − 2 L i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (46)
Applying the cut reduction
Since each F i F_i F i is supported on [ N ] d [N]^d [ N ] d , the sum S β S_\beta S β may be taken over ( [ N ] d ) K ([N]^d)^K ([ N ] d ) K . For a cut S ∣ T S \mid T S ∣ T , take X i = [ N ] d X_i=[N]^d X i = [ N ] d with counting measure and set f i = F i f_i=F_i f i = F i . The crossing part of the phase is
Q β S , T ( z S , z T ) = − 2 ∑ i ∈ S , j ∈ T β i j z i ⋅ z j , Q_{\beta}^{S,T}(z_S,z_T)=-2\sum_{i\in S,j\in T}\beta_{ij}z_i\cdot z_j, Q β S , T ( z S , z T ) = − 2 i ∈ S , j ∈ T ∑ β ij z i ⋅ z j ,
so the cut operator in Lemma 5.1 is exactly K N , d S , T ( − 2 β S , T ) K_{N,d}^{S,T}(-2\beta_{S,T}) K N , d S , T ( − 2 β S , T ) . That lemma therefore gives
∣ S β ∣ ≤ ∥ K N , d S , T ( − 2 β S , T ) ∥ 2 → 2 ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . |S_\beta| \le\|K_{N,d}^{S,T}(-2\beta_{S,T})\|_{2\to2}\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. ∣ S β ∣ ≤ ∥ K N , d S , T ( − 2 β S , T ) ∥ 2 → 2 i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) .
Applying Lemma 5.2 now gives the required cut-operator estimate directly.
Proposition 6.2 (Cut-operator estimate). *For every β ∈ T L \beta\in\mathbb{T}^L β ∈ T L , cut S ∣ T S\mid T S ∣ T , and functions F 0 , … , F K − 1 : Z d → C F_0,\ldots,F_{K-1}:\mathbb{Z}^d\to\mathbb{C} F 0 , … , F K − 1 : Z d → C supported on [ N ] d [N]^d [ N ] d ,
∣ S β ∣ ≤ N K d / 2 m N S , T ( − 2 β S , T ) d ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (47) |S_\beta|\le N^{Kd/2}m_N^{S,T}(-2\beta_{S,T})^d\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. \tag*{(47)} ∣ S β ∣ ≤ N K d /2 m N S , T ( − 2 β S , T ) d i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (47)
The large-sieve estimate
We now prove the scalar Weyl estimate stated in Lemma 2.3 by applying the analytic large sieve.
Proof of Lemma 2.3. For the proof, write P : = R P:=R P := R and θ : = ξ \theta:=\xi θ := ξ . Dirichlet’s approximation theorem gives coprime integers a , q a,q a , q such that
1 ≤ q ≤ P 2 Q , ∥ θ − a / q ∥ T ≤ Q q P 2 . 1\le q\le\frac{P^2}{Q},\qquad\|\theta-a/q\|_{\mathbb{T}}\le\frac{Q}{qP^2}. 1 ≤ q ≤ Q P 2 , ∥ θ − a / q ∥ T ≤ q P 2 Q .
If q ≤ Q q\le Q q ≤ Q , this contradicts the hypothesis of the lemma. Therefore Q < q ≤ P 2 / Q Q<q\le P^2/Q Q < q ≤ P 2 / Q . The upper bound on q q q also gives ∥ θ − a / q ∥ T ≤ Q / ( q P 2 ) ≤ 1 / q 2 \|\theta-a/q\|_{\mathbb{T}}\le Q/(qP^2)\le1/q^2 ∥ θ − a / q ∥ T ≤ Q / ( q P 2 ) ≤ 1/ q 2 .
If q < 8 q<8 q < 8 , then Q < 8 Q<8 Q < 8 , and the conclusion follows immediately from m P ( θ ) 2 ≤ 1 ≤ 8 Q − 1 m_P(\theta)^2\le1\le8Q^{-1} m P ( θ ) 2 ≤ 1 ≤ 8 Q − 1 . We may therefore suppose that q ≥ 8 q\ge8 q ≥ 8 .
Partition [ P ] [P] [ P ] into j ≪ 1 + P / q j\ll1+P/q j ≪ 1 + P / q intervals I 1 , … , I j I_1,\ldots,I_j I 1 , … , I j , each of diameter at most q / 8 q/8 q /8 . Thus, for every 1 ≤ r ≤ j 1\le r\le j 1 ≤ r ≤ j ,
∣ u − u ′ ∣ ≤ q 8 ( u , u ′ ∈ I r ) . |u-u'|\le\frac{q}{8}\qquad(u,u'\in I_r). ∣ u − u ′ ∣ ≤ 8 q ( u , u ′ ∈ I r ) .
Fix one such interval I r I_r I r . Suppose u , u ′ ∈ I r u,u'\in I_r u , u ′ ∈ I r are distinct. Since ( a , q ) = 1 (a,q)=1 ( a , q ) = 1 and ∣ u − u ′ ∣ ≤ q / 8 < q |u-u'|\le q/8<q ∣ u − u ′ ∣ ≤ q /8 < q , the integer a ( u − u ′ ) a(u-u') a ( u − u ′ ) is not divisible by q q q , so ∥ a ( u − u ′ ) / q ∥ T ≥ 1 / q \|a(u-u')/q\|_{\mathbb{T}}\ge1/q ∥ a ( u − u ′ ) / q ∥ T ≥ 1/ q . Moreover,
∣ u − u ′ ∣ ∥ θ − a / q ∥ T ≤ q 8 ⋅ 1 q 2 = 1 8 q . |u-u'|\|\theta-a/q\|_{\mathbb{T}}\le\frac{q}{8}\cdot\frac{1}{q^2}=\frac{1}{8q}. ∣ u − u ′ ∣∥ θ − a / q ∥ T ≤ 8 q ⋅ q 2 1 = 8 q 1 .
It follows from the reverse triangle inequality that
∥ θ u − θ u ′ ∥ T = ∥ θ ( u − u ′ ) ∥ T ≥ ∥ a ( u − u ′ ) q ∥ T − ∥ ( u − u ′ ) ( θ − a q ) ∥ T ≥ ∥ a ( u − u ′ ) q ∥ T − ∣ u − u ′ ∣ ∥ θ − a q ∥ T ≥ 1 q − 1 8 q = 7 8 q . \begin{aligned}
\lVert\theta u-\theta u'\rVert_{\mathbb{T}}&=\lVert\theta(u-u')\rVert_{\mathbb{T}}\\
&\ge\left\lVert\frac{a(u-u')}{q}\right\rVert_{\mathbb{T}}-\left\lVert(u-u')\left(\theta-\frac{a}{q}\right)\right\rVert_{\mathbb{T}}\\
&\ge\left\lVert\frac{a(u-u')}{q}\right\rVert_{\mathbb{T}}-|u-u'|\left\lVert\theta-\frac{a}{q}\right\rVert_{\mathbb{T}}\\
&\ge\frac{1}{q}-\frac{1}{8q}=\frac{7}{8q}.
\end{aligned} ∥ θ u − θ u ′ ∥ T = ∥ θ ( u − u ′ ) ∥ T ≥ q a ( u − u ′ ) T − ( u − u ′ ) ( θ − q a ) T ≥ q a ( u − u ′ ) T − ∣ u − u ′ ∣ θ − q a T ≥ q 1 − 8 q 1 = 8 q 7 .
Thus the points { θ u : u ∈ I r } \{\theta u:u\in I_r\} { θ u : u ∈ I r } are pairwise separated in T \mathbb{T} T by at least 7 / ( 8 q ) 7/(8q) 7/ ( 8 q ) . Applying the analytic large sieve [18 ] to the trigonometric polynomial ∑ s ∈ [ P ] h ( s ) e ( s x ) \sum_{s\in[P]}h(s)e(sx) ∑ s ∈ [ P ] h ( s ) e ( s x ) at the points { θ u : u ∈ I r } \{\theta u:u\in I_r\} { θ u : u ∈ I r } and using their separation yields
∑ u ∈ I r ∣ ∑ s ∈ [ P ] e ( θ u s ) h ( s ) ∣ 2 ≤ ( P + 8 q 7 ) ∥ h ∥ ℓ 2 ( [ P ] ) 2 . \sum_{u\in I_r}\left|\sum_{s\in[P]}e(\theta us)h(s)\right|^2\le\left(P+\frac{8q}{7}\right)\lVert h\rVert_{\ell^2([P])}^2. u ∈ I r ∑ s ∈ [ P ] ∑ e ( θ u s ) h ( s ) 2 ≤ ( P + 7 8 q ) ∥ h ∥ ℓ 2 ([ P ]) 2 .
Summing this inequality over r ∈ [ j ] r\in[j] r ∈ [ j ] and using the fact that the intervals I r I_r I r partition [ P ] [P] [ P ] , we obtain
∑ u ∈ [ P ] ∣ ∑ s ∈ [ P ] e ( θ u s ) h ( s ) ∣ 2 ≪ ( 1 + P q ) ( P + q ) ∥ h ∥ ℓ 2 ( [ P ] ) 2 . \sum_{u\in[P]}\left|\sum_{s\in[P]}e(\theta us)h(s)\right|^2\ll\left(1+\frac{P}{q}\right)(P+q)\lVert h\rVert_{\ell^2([P])}^2. u ∈ [ P ] ∑ s ∈ [ P ] ∑ e ( θ u s ) h ( s ) 2 ≪ ( 1 + q P ) ( P + q ) ∥ h ∥ ℓ 2 ([ P ]) 2 .
Taking the supremum over h ∈ ℓ 2 ( [ P ] ) h\in\ell^2([P]) h ∈ ℓ 2 ([ P ]) with ∥ h ∥ ℓ 2 ( [ P ] ) ≤ 1 \lVert h\rVert_{\ell^2([P])}\le1 ∥ h ∥ ℓ 2 ([ P ]) ≤ 1 yields
∥ K P ( θ ) ∥ 2 → 2 2 ≪ ( 1 + P q ) ( P + q ) . \lVert K_P(\theta)\rVert_{2\to2}^2\ll\left(1+\frac{P}{q}\right)(P+q). ∥ K P ( θ ) ∥ 2 → 2 2 ≪ ( 1 + q P ) ( P + q ) .
Since m P ( θ ) = P − 1 ∥ K P ( θ ) ∥ 2 → 2 m_P(\theta)=P^{-1}\lVert K_P(\theta)\rVert_{2\to2} m P ( θ ) = P − 1 ∥ K P ( θ ) ∥ 2 → 2 , we conclude that
m P ( θ ) 2 ≪ P − 2 ( 1 + P q ) ( P + q ) ≪ q − 1 + P − 1 + q P − 2 ≪ Q − 1 . m_P(\theta)^2\ll P^{-2}\left(1+\frac{P}{q}\right)(P+q)\ll q^{-1}+P^{-1}+qP^{-2}\ll Q^{-1}. m P ( θ ) 2 ≪ P − 2 ( 1 + q P ) ( P + q ) ≪ q − 1 + P − 1 + q P − 2 ≪ Q − 1 .
Here we used
q > Q , Q ≤ P , q ≤ P 2 Q . q>Q,\qquad Q\le P,\qquad q\le\frac{P^2}{Q}. q > Q , Q ≤ P , q ≤ Q P 2 .
Proof of the minor arc estimate
We now prove the minor arc estimate by combining fractional Hölder, the large-sieve estimate, and the high moment estimate.
Proof of Proposition 6.1. Put τ : = d ⌊ K 2 / 4 ⌋ / ( 2 L ) \tau:=d\lfloor K^2/4\rfloor/(2L) τ := d ⌊ K 2 /4 ⌋ / ( 2 L ) . Since 2 τ w K = d / ∣ B K ∣ 2\tau w_K=d/|\mathcal{B}_K| 2 τ w K = d /∣ B K ∣ , taking the geometric mean of Proposition 6.2 over all balanced cuts gives
∣ S β ∣ ≤ N K d / 2 ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (48) |S_\beta|\le N^{Kd/2}\prod_{S|T\in\mathcal{B}_K}m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K}\prod_{i=0}^{K-1}\lVert F_i\rVert_{\ell^2(\mathbb{Z}^d)}. \tag*{(48)} ∣ S β ∣ ≤ N K d /2 S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (48)
The dimension hypothesis gives
τ ≥ ( 4 K + 4 ) ( K 2 − 1 ) 8 L = ( K + 1 ) 2 K > K + 2. \tau\ge\frac{(4K+4)(K^2-1)}{8L}=\frac{(K+1)^2}{K}>K+2. τ ≥ 8 L ( 4 K + 4 ) ( K 2 − 1 ) = K ( K + 1 ) 2 > K + 2.
Set γ : = τ − ( K + 2 ) > 0 \gamma:= \tau- (K + 2) > 0 γ := τ − ( K + 2 ) > 0 . In view of (31) , (32) , and (48) , it remains to prove
∫ Q ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K d β ≪ K , d Q − γ N − 2 L . (49) \int_{\mathbb{Q}} \prod_{S|T\in\mathcal{B}_K} m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K}\,d\beta\ll_{K,d} Q^{-\gamma}N^{-2L}. \tag*{(49)} ∫ Q S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K d β ≪ K , d Q − γ N − 2 L . (49)
For each e ∈ E e\in\mathcal{E} e ∈ E , put
I e : = ∫ T L 1 m Q ( 1 ) ( − 2 β e ) ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K d β . I_e:=\int_{\mathbb{T}^L} \mathbf{1}_{m_{\mathbb{Q}}^{(1)}}(-2\beta_e)\prod_{S|T\in\mathcal{B}_K}m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K}\,d\beta. I e := ∫ T L 1 m Q ( 1 ) ( − 2 β e ) S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K d β .
By (45) and nonnegativity,
∫ Q ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K d β ≤ ∑ e ∈ E I e . \int_{\mathbb{Q}}\prod_{S|T\in\mathcal{B}_K}m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K}\,d\beta\leq\sum_{e\in\mathcal{E}}I_e. ∫ Q S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K d β ≤ e ∈ E ∑ I e .
It therefore suffices to prove
I e ≪ K , d Q − γ N − 2 L (50) I_e\ll_{K,d}Q^{-\gamma}N^{-2L} \tag*{(50)} I e ≪ K , d Q − γ N − 2 L (50)
for every e ∈ E e\in\mathcal{E} e ∈ E .
Fix e ∈ E e\in\mathcal{E} e ∈ E . In applying Lemma 5.5, take J = ∣ B K ∣ J=|\mathcal{B}_K| J = ∣ B K ∣ and label the indices 1 , … , J 1,\ldots,J 1 , … , J by the balanced cuts S ∣ T ∈ B K S\mid T\in\mathcal{B}_K S ∣ T ∈ B K . Thus, for the index j j j corresponding to S ∣ T S\mid T S ∣ T , the set E j \mathcal{E}_j E j in that lemma is the set of edges crossing S ∣ T S\mid T S ∣ T , and the weight c j c_j c j is w K w_K w K . We write f S , T f_{S,T} f S , T for the corresponding function f j f_j f j .
For each balanced cut S ∣ T S\mid T S ∣ T , define
f S , T ( β S , T ) : = { 1 m Q ( 1 ) ( − 2 β e ) m N S , T ( − 2 β S , T ) 2 τ , e crosses S ∣ T , m N S , T ( − 2 β S , T ) 2 τ , e does not cross S ∣ T . f_{S,T}(\beta_{S,T}):=
\begin{cases}
\mathbf{1}_{m_{\mathbb{Q}}^{(1)}}(-2\beta_e)m_N^{S,T}(-2\beta_{S,T})^{2\tau}, & e\text{ crosses }S\mid T,\\
m_N^{S,T}(-2\beta_{S,T})^{2\tau}, & e\text{ does not cross }S\mid T.
\end{cases} f S , T ( β S , T ) := { 1 m Q ( 1 ) ( − 2 β e ) m N S , T ( − 2 β S , T ) 2 τ , m N S , T ( − 2 β S , T ) 2 τ , e crosses S ∣ T , e does not cross S ∣ T .
When e e e crosses S ∣ T S\mid T S ∣ T , the coordinate β e \beta_e β e is one of the coordinates in β S , T \beta_{S,T} β S , T , so the first function is indeed defined on the product of the coordinate spaces indexed by E j \mathcal{E}_j E j .
For every e ′ ∈ E e'\in\mathcal{E} e ′ ∈ E , Lemma 5.4 gives
∑ S ∣ T ∈ B K e ′ crosses S ∣ T w K = 1 , \sum_{\substack{S\mid T\in\mathcal{B}_K\\e'\text{ crosses }S\mid T}}w_K=1, S ∣ T ∈ B K e ′ crosses S ∣ T ∑ w K = 1 ,
which is exactly the hypothesis of Lemma 5.5. Moreover,
∏ S ∣ T ∈ B K f S , T ( β S , T ) w K = ( ∏ S ∣ T ∈ B K e crosses S ∣ T 1 m Q ( 1 ) ( − 2 β e ) w K ) ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K = 1 m Q ( 1 ) ( − 2 β e ) ∏ S ∣ T ∈ B K m N S , T ( − 2 β S , T ) 2 τ w K , \begin{aligned}
\prod_{S\mid T\in\mathcal{B}_K}f_{S,T}(\beta_{S,T})^{w_K}
=
\left(\prod_{\substack{S\mid T\in\mathcal{B}_K\\e\text{ crosses }S\mid T}}\mathbf{1}_{m_{\mathbb{Q}}^{(1)}}(-2\beta_e)^{w_K}\right)
\prod_{S\mid T\in\mathcal{B}_K}m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K} \\
&=
\mathbf{1}_{m_{\mathbb{Q}}^{(1)}}(-2\beta_e)
\prod_{S\mid T\in\mathcal{B}_K}m_N^{S,T}(-2\beta_{S,T})^{2\tau w_K},
\end{aligned} S ∣ T ∈ B K ∏ f S , T ( β S , T ) w K = S ∣ T ∈ B K e crosses S ∣ T ∏ 1 m Q ( 1 ) ( − 2 β e ) w K S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K = 1 m Q ( 1 ) ( − 2 β e ) S ∣ T ∈ B K ∏ m N S , T ( − 2 β S , T ) 2 τ w K ,
where the last equality follows from (43) . Hence
I e = ∫ T L ∏ S ∣ T ∈ B K f S , T ( β S , T ) w K d β . I_e=\int_{\mathbb{T}^L}\prod_{S\mid T\in\mathcal{B}_K}f_{S,T}(\beta_{S,T})^{w_K}\,d\beta. I e = ∫ T L S ∣ T ∈ B K ∏ f S , T ( β S , T ) w K d β .
Lemma 5.5 therefore gives
I e ≤ ∏ S ∣ T ∈ B K e crosses S ∣ T ( ∫ T ∣ S ∣ ∣ T ∣ 1 m Q ( 1 ) ( − 2 β e ) m N S , T ( − 2 β S , T ) 2 τ d β S , T ) w K × ∏ S ∣ T ∈ B K e does not cross S ∣ T ( ∫ T ∣ S ∣ ∣ T ∣ m N S , T ( − 2 β S , T ) 2 τ d β S , T ) w K . (51) I_e \le\prod_{\substack{S|T\in\mathcal{B}_K\\ e\text{ crosses }S|T}}\left(\int_{\mathbb{T}^{|S||T|}} \mathbf{1}_{m_Q^{(1)}}(-2\beta_e)m_N^{S,T}(-2\beta_{S,T})^{2\tau}\,d\beta_{S,T}\right)^{w_K}
\times\prod_{\substack{S|T\in\mathcal{B}_K\\ e\text{ does not cross }S|T}}\left(\int_{\mathbb{T}^{|S||T|}}m_N^{S,T}(-2\beta_{S,T})^{2\tau}\,d\beta_{S,T}\right)^{w_K}. \tag*{(51)} I e ≤ S ∣ T ∈ B K e crosses S ∣ T ∏ ( ∫ T ∣ S ∣∣ T ∣ 1 m Q ( 1 ) ( − 2 β e ) m N S , T ( − 2 β S , T ) 2 τ d β S , T ) w K × S ∣ T ∈ B K e does not cross S ∣ T ∏ ( ∫ T ∣ S ∣∣ T ∣ m N S , T ( − 2 β S , T ) 2 τ d β S , T ) w K . (51)
We now estimate the integrals on the right. Fix a balanced cut S ∣ T S\mid T S ∣ T . The coordinatewise map
( β i j ) i ∈ S , j ∈ T ⟼ ( − 2 β i j ) i ∈ S , j ∈ T (\beta_{ij})_{i\in S,j\in T}\longmapsto(-2\beta_{ij})_{i\in S,j\in T} ( β ij ) i ∈ S , j ∈ T ⟼ ( − 2 β ij ) i ∈ S , j ∈ T
preserves normalized Haar measure on T ∣ S ∣ ∣ T ∣ \mathbb{T}^{|S||T|} T ∣ S ∣∣ T ∣ . Hence Lemma 2.1 gives
∫ T ∣ S ∣ ∣ T ∣ m N S , T ( − 2 β S , T ) 2 K + 4 d β S , T ≪ K N − 2 ∣ S ∣ ∣ T ∣ . (52) \int_{\mathbb{T}^{|S||T|}}m_N^{S,T}(-2\beta_{S,T})^{2K+4}\,d\beta_{S,T}\ll_K N^{-2|S||T|}. \tag*{(52)} ∫ T ∣ S ∣∣ T ∣ m N S , T ( − 2 β S , T ) 2 K + 4 d β S , T ≪ K N − 2∣ S ∣∣ T ∣ . (52)
Since 0 ≤ m N S , T ( − 2 β S , T ) ≤ 1 0\le m_N^{S,T}(-2\beta_{S,T})\le1 0 ≤ m N S , T ( − 2 β S , T ) ≤ 1 and 2 τ > 2 K + 4 2\tau>2K+4 2 τ > 2 K + 4 , (52) gives
∫ T ∣ S ∣ ∣ T ∣ m N S , T ( − 2 β S , T ) 2 τ d β S , T ≪ K N − 2 ∣ S ∣ ∣ T ∣ . (53) \int_{\mathbb{T}^{|S||T|}}m_N^{S,T}(-2\beta_{S,T})^{2\tau}\,d\beta_{S,T}\ll_K N^{-2|S||T|}. \tag*{(53)} ∫ T ∣ S ∣∣ T ∣ m N S , T ( − 2 β S , T ) 2 τ d β S , T ≪ K N − 2∣ S ∣∣ T ∣ . (53)
Suppose that e e e crosses S ∣ T S\mid T S ∣ T . Whenever − 2 β e ∈ m Q ( 1 ) -2\beta_e\in\mathfrak{m}_Q^{(1)} − 2 β e ∈ m Q ( 1 ) , the definition of m Q ( 1 ) \mathfrak{m}_Q^{(1)} m Q ( 1 ) and the inequality N ≥ R N\ge R N ≥ R show that − 2 β e -2\beta_e − 2 β e satisfies the hypothesis of Lemma 2.3 with P = N P=N P = N ; indeed, Q / ( q N 2 ) ≤ Q / ( q R 2 ) Q/(qN^2)\le Q/(qR^2) Q / ( q N 2 ) ≤ Q / ( q R 2 ) . Therefore Lemma 5.3 gives
m N S , T ( − 2 β S , T ) ≤ m N ( − 2 β e ) ≪ Q − 1 / 2 . m_N^{S,T}(-2\beta_{S,T})\le m_N(-2\beta_e)\ll Q^{-1/2}. m N S , T ( − 2 β S , T ) ≤ m N ( − 2 β e ) ≪ Q − 1/2 .
Since 2 τ = 2 K + 4 + 2 γ 2\tau=2K+4+2\gamma 2 τ = 2 K + 4 + 2 γ , we have, on this set,
m N S , T ( − 2 β S , T ) 2 τ ≪ K , d Q − γ m N S , T ( − 2 β S , T ) 2 K + 4 . m_N^{S,T}(-2\beta_{S,T})^{2\tau}\ll_{K,d}Q^{-\gamma}m_N^{S,T}(-2\beta_{S,T})^{2K+4}. m N S , T ( − 2 β S , T ) 2 τ ≪ K , d Q − γ m N S , T ( − 2 β S , T ) 2 K + 4 .
Using (52) , we obtain
∫ T ∣ S ∣ ∣ T ∣ 1 m Q ( 1 ) ( − 2 β e ) m N S , T ( − 2 β S , T ) 2 τ d β S , T ≪ K , d Q − γ N − 2 ∣ S ∣ ∣ T ∣ . (54) \int_{\mathbb{T}^{|S||T|}}\mathbf{1}_{\mathfrak{m}_Q^{(1)}}(-2\beta_e)m_N^{S,T}(-2\beta_{S,T})^{2\tau}\,d\beta_{S,T}\ll_{K,d}Q^{-\gamma}N^{-2|S||T|}. \tag*{(54)} ∫ T ∣ S ∣∣ T ∣ 1 m Q ( 1 ) ( − 2 β e ) m N S , T ( − 2 β S , T ) 2 τ d β S , T ≪ K , d Q − γ N − 2∣ S ∣∣ T ∣ . (54)
For the cuts crossed by e e e , (54) contributes both a factor Q − γ w K Q^{-\gamma w_K} Q − γ w K and a factor N − 2 w K ∣ S ∣ ∣ T ∣ N^{-2w_K|S||T|} N − 2 w K ∣ S ∣∣ T ∣ to the fractional Hölder estimate (51) . For the remaining cuts, (53) contributes the factor N − 2 w K ∣ S ∣ ∣ T ∣ N^{-2w_K|S||T|} N − 2 w K ∣ S ∣∣ T ∣ . Hence
I e ≪ K , d Q − γ w K # { S ∣ T ∈ B K : e crosses S ∣ T } N − 2 w K ∑ S ∣ T ∈ B K ∣ S ∣ ∣ T ∣ . I_e\ll_{K,d}Q^{-\gamma w_K\#\{S|T\in\mathcal{B}_K:e\text{ crosses }S|T\}}N^{-2w_K\sum_{S|T\in\mathcal{B}_K}|S||T|}. I e ≪ K , d Q − γ w K # { S ∣ T ∈ B K : e crosses S ∣ T } N − 2 w K ∑ S ∣ T ∈ B K ∣ S ∣∣ T ∣ .
The fractional-cover identity (43) gives
w K # { S ∣ T ∈ B K : e crosses S ∣ T } = 1. w_K\#\{S\mid T\in\mathcal{B}_K:e\text{ crosses }S\mid T\}=1. w K # { S ∣ T ∈ B K : e crosses S ∣ T } = 1.
Moreover, every balanced cut has ∣ S ∣ ∣ T ∣ = ⌊ K 2 / 4 ⌋ |S||T|=\lfloor K^2/4\rfloor ∣ S ∣∣ T ∣ = ⌊ K 2 /4 ⌋ , so the definition of w K w_K w K gives
w K ∑ S ∣ T ∈ B K ∣ S ∣ ∣ T ∣ = w K ∣ B K ∣ ⌊ K 2 / 4 ⌋ = L . (55) w_K\sum_{S|T\in\mathcal{B}_K}|S||T|=w_K|\mathcal{B}_K|\lfloor K^2/4\rfloor=L. \tag*{(55)} w K S ∣ T ∈ B K ∑ ∣ S ∣∣ T ∣ = w K ∣ B K ∣ ⌊ K 2 /4 ⌋ = L . (55)
Putting these facts together proves (50) , and hence (49) . Combining that estimate with (31) , (32) , and (48) gives
∣ Λ N m Q ( F 0 , … , F K − 1 ) ∣ ≪ Δ , d Q − γ N K d / 2 − 2 L ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . |\Lambda_N^{\mathfrak{m}_Q}(F_0,\ldots,F_{K-1})|\ll_{\Delta,d}Q^{-\gamma}N^{Kd/2-2L}\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. ∣ Λ N m Q ( F 0 , … , F K − 1 ) ∣ ≪ Δ , d Q − γ N K d /2 − 2 L i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) .
Finally, N ≥ 2 / σ N\ge2/\sigma N ≥ 2/ σ implies R = ⌊ σ N ⌋ ≥ σ N / 2 R=\lfloor\sigma N\rfloor\ge\sigma N/2 R = ⌊ σ N ⌋ ≥ σ N /2 , so the second bound in (46) follows as well.
The major arcs
Throughout this section assume
R ≥ 4 Q L ( L + 3 ) + 3 . (56) R \ge4Q^{L(L+3)+3}. \tag*{(56)} R ≥ 4 Q L ( L + 3 ) + 3 . (56)
For 1 ≤ q ≤ 2 Q L 1 \le q \le2Q^L 1 ≤ q ≤ 2 Q L , put
N q : = ⌊ R q Q L ( L + 2 ) + 3 ⌋ . (57) N_q := \left\lfloor\frac{R}{qQ^{L(L+2)+3}} \right\rfloor. \tag*{(57)} N q := ⌊ q Q L ( L + 2 ) + 3 R ⌋ . (57)
Since q ≤ 2 Q L q \le2Q^L q ≤ 2 Q L , condition (56) gives N q ≥ 1 N_q \ge1 N q ≥ 1 . Define the probability measure μ q \mu_q μ q on Z d \mathbb{Z}^d Z d by
μ q ( x ) : = 1 N q d 1 q { 0 , … , N q − 1 } d ( x ) . \mu_q(x) := \frac{1}{N_q^d}\mathbf{1}_{q\{0,\ldots,N_q-1\}^d}(x). μ q ( x ) := N q d 1 1 q { 0 , … , N q − 1 } d ( x ) .
Thus, for every finitely supported F : Z d → C F:\mathbb{Z}^d \to\mathbb{C} F : Z d → C ,
( μ q ∗ F ) ( y ) = 1 N q d ∑ h ∈ { 0 , … , N q − 1 } d F ( y − q h ) . (\mu_q * F)(y) = \frac{1}{N_q^d}\sum_{h\in\{0,\ldots,N_q-1\}^d} F(y-qh). ( μ q ∗ F ) ( y ) = N q d 1 h ∈ { 0 , … , N q − 1 } d ∑ F ( y − q h ) .
The goal of the section is to prove the following major arc estimate.
Proposition 7.1 (major arc estimate). Assume 2 ≤ Q ≤ R 2 \le Q \le R 2 ≤ Q ≤ R , d ≥ 4 K + 4 d \ge4K+4 d ≥ 4 K + 4 , and (56) . There is η = η ( K , d ) > 1 \eta=\eta(K,d)>1 η = η ( K , d ) > 1 such that, for all functions F 0 , … , F K − 1 : Z d → C F_0,\ldots,F_{K-1}:\mathbb{Z}^d\to\mathbb{C} F 0 , … , F K − 1 : Z d → C supported on [ N ] d [N]^d [ N ] d ,
∣ Λ N m Q ( F 0 , … , F K − 1 ) ∣ ≪ Δ , d , σ R ( K − 1 ) d − 2 L ∑ q ≤ 2 Q L q − 1 − η ∏ i = 0 K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) + Q − 2 R K d / 2 − 2 L ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (58) \left|\Lambda_N^{\mathfrak m_Q}(F_0,\ldots,F_{K-1})\right| \ll_{\Delta,d,\sigma} R^{(K-1)d-2L}\sum_{q\le2Q^L}q^{-1-\eta}\prod_{i=0}^{K-1}\|\mu_q*F_i\|_{\ell^K(\mathbb{Z}^d)}
+Q^{-2}R^{Kd/2-2L}\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. \tag*{(58)} Λ N m Q ( F 0 , … , F K − 1 ) ≪ Δ , d , σ R ( K − 1 ) d − 2 L q ≤ 2 Q L ∑ q − 1 − η i = 0 ∏ K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) + Q − 2 R K d /2 − 2 L i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (58)
For q ≥ 1 q\ge1 q ≥ 1 , a ∈ ( Z / q Z ) L a\in(\mathbb{Z}/q\mathbb{Z})^L a ∈ ( Z / q Z ) L , θ ∈ T L \theta\in\mathbb{T}^L θ ∈ T L , and finitely supported functions G i : Z d × ( Z / q Z ) d → C G_i:\mathbb{Z}^d\times(\mathbb{Z}/q\mathbb{Z})^d\to\mathbb{C} G i : Z d × ( Z / q Z ) d → C , define the major arc form
M q , a , θ ( G 0 , … , G K − 1 ) : = E r mod q ∑ u ∈ ( Z d ) K e q ( Q a ( r ) ) e ( Q θ ( u ) ) ∏ i = 0 K − 1 G i ( u i , r i ) . (59) \mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1}) := \mathbb{E}_{r\mathbin{\operatorname{mod}}q}\sum_{u\in(\mathbb{Z}^d)^K}e_q(Q_a(r))e(Q_\theta(u))\prod_{i=0}^{K-1}G_i(u_i,r_i). \tag*{(59)} M q , a , θ ( G 0 , … , G K − 1 ) := E r mod q u ∈ ( Z d ) K ∑ e q ( Q a ( r )) e ( Q θ ( u )) i = 0 ∏ K − 1 G i ( u i , r i ) . (59)
Here r = ( r 0 , … , r K − 1 ) r=(r_0,\ldots,r_{K-1}) r = ( r 0 , … , r K − 1 ) ranges over ( Z / q Z ) d K (\mathbb{Z}/q\mathbb{Z})^{dK} ( Z / q Z ) d K , and u = ( u 0 , … , u K − 1 ) u=(u_0,\ldots,u_{K-1}) u = ( u 0 , … , u K − 1 ) ranges over ( Z d ) K (\mathbb{Z}^d)^K ( Z d ) K . This is a form of the general type controlled by the cut-operator estimates, with each vertex variable given by the pair ( u i , r i ) (u_i,r_i) ( u i , r i ) . Its kernel has a complete-sum factor in the residue variables and a discrete factor in the lattice variables. The form need not factor, since each G i G_i G i may depend on both u i u_i u i and r i r_i r i .
For every ( u , r ) ∈ Z d × ( Z / q Z ) d (u,r)\in\mathbb{Z}^d\times(\mathbb{Z}/q\mathbb{Z})^d ( u , r ) ∈ Z d × ( Z / q Z ) d , there is a unique y ∈ Z d y\in\mathbb{Z}^d y ∈ Z d satisfying
y ≡ r ( m o d q ) , u − y ∈ { 0 , … , q − 1 } d . y\equiv r\pmod q,\qquad u-y\in\{0,\ldots,q-1\}^d. y ≡ r ( mod q ) , u − y ∈ { 0 , … , q − 1 } d .
For an input F i : Z d → C F_i:\mathbb{Z}^d\to\mathbb{C} F i : Z d → C , define G i , q : Z d × ( Z / q Z ) d → C G_{i,q}:\mathbb{Z}^d\times(\mathbb{Z}/q\mathbb{Z})^d\to\mathbb{C} G i , q : Z d × ( Z / q Z ) d → C by
G i , q ( u , r ) : = ( μ q ∗ F i ) ( y ) , (60) G_{i,q}(u,r):=(\mu_q*F_i)(y), \tag*{(60)} G i , q ( u , r ) := ( μ q ∗ F i ) ( y ) , (60)
where y y y is the unique lattice point just described. Thus r r r is the residue class modulo q q q of the lattice point at which μ q ∗ F i \mu_q*F_i μ q ∗ F i is evaluated.
Lemma 7.2. Suppose 1 ≤ q ≤ 2 Q L 1 \le q \le2Q^{L} 1 ≤ q ≤ 2 Q L , a ∈ ( Z / q Z ) L a \in(\mathbb{Z}/q\mathbb{Z})^{L} a ∈ ( Z / q Z ) L , and ∥ θ ∥ ∞ ≤ Q / R 2 \lVert\theta\rVert_{\infty} \le Q/R^{2} ∥ θ ∥ ∞ ≤ Q / R 2 . Then, for all functions F 0 , … , F K − 1 : Z d → C F_{0},\ldots,F_{K-1}:\mathbb{Z}^{d}\to\mathbb{C} F 0 , … , F K − 1 : Z d → C supported on [ N ] d [N]^{d} [ N ] d ,
∣ S a / q + θ ( F 0 , … , F K − 1 ) − M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ ≪ K , d , σ Q − L ( L + 2 ) − 2 N K d / 2 ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (61) \left|\mathcal{S}_{a/q+\theta}(F_{0},\ldots,F_{K-1})-\mathcal{M}_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})\right|\ll_{K,d,\sigma} Q^{-L(L+2)-2}N^{Kd/2}\prod_{i=0}^{K-1}\lVert F_{i}\rVert_{\ell^{2}(\mathbb{Z}^{d})}. \tag*{(61)} S a / q + θ ( F 0 , … , F K − 1 ) − M q , a , θ ( G 0 , q , … , G K − 1 , q ) ≪ K , d , σ Q − L ( L + 2 ) − 2 N K d /2 i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (61)
Proof. Since every ∣ z i − z j ∣ 2 \lvert z_{i}-z_{j}\rvert^{2} ∣ z i − z j ∣ 2 is an integer,
e ( Q a / q + θ ( z ) ) = e q ( Q a ( z ) ) e ( Q θ ( z ) ) . e(\mathcal{Q}_{a/q+\theta}(z))=e_{q}(\mathcal{Q}_{a}(z))e(\mathcal{Q}_{\theta}(z)). e ( Q a / q + θ ( z )) = e q ( Q a ( z )) e ( Q θ ( z )) .
Consequently,
S a / q + θ ( F 0 , … , F K − 1 ) = ∑ z ∈ ( Z d ) K e q ( Q a ( z ) ) e ( Q θ ( z ) ) ∏ i = 0 K − 1 F i ( z i ) . (62) \mathcal{S}_{a/q+\theta}(F_{0},\ldots,F_{K-1})=\sum_{z\in(\mathbb{Z}^{d})^{K}}e_{q}(\mathcal{Q}_{a}(z))e(\mathcal{Q}_{\theta}(z))\prod_{i=0}^{K-1}F_{i}(z_{i}). \tag*{(62)} S a / q + θ ( F 0 , … , F K − 1 ) = z ∈ ( Z d ) K ∑ e q ( Q a ( z )) e ( Q θ ( z )) i = 0 ∏ K − 1 F i ( z i ) . (62)
We now rewrite the major arc form in the variables appearing in (62) . Fix r = ( r 0 , … , r K − 1 ) r=(r_{0},\ldots,r_{K-1}) r = ( r 0 , … , r K − 1 ) . For each 0 ≤ i ≤ K − 1 0\le i\le K-1 0 ≤ i ≤ K − 1 , every u i ∈ Z d u_{i}\in\mathbb{Z}^{d} u i ∈ Z d has a unique representation
u i = y i + s i , y i ≡ r i ( m o d q ) , s i ∈ { 0 , … , q − 1 } d . u_{i}=y_{i}+s_{i},\qquad y_{i}\equiv r_{i}\pmod q,\qquad s_{i}\in\{0,\ldots,q-1\}^{d}. u i = y i + s i , y i ≡ r i ( mod q ) , s i ∈ { 0 , … , q − 1 } d .
It follows from (60) that
M q , a , θ ( G 0 , q , … , G K − 1 , q ) = E r m o d q ∑ y ∈ ( Z d ) K y i ≡ r i ( m o d q ) ( 0 ≤ i ≤ K − 1 ) e q ( Q a ( r ) ) ∏ i = 0 K − 1 ( μ q ∗ F i ) ( y i ) × ∑ s i ∈ { 0 , … , q − 1 } d 0 ≤ i ≤ K − 1 e ( Q θ ( y + s ) ) . \begin{aligned}
\mathcal{M}_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})
={}&\mathbb{E}_{r\ \mathrm{mod}\ q}
\sum_{\substack{y\in(\mathbb{Z}^{d})^{K}\\ y_{i}\equiv r_{i\ }(\mathrm{mod}\ q)\ (0\le i\le K-1)}}
e_{q}(\mathcal{Q}_{a}(r))\prod_{i=0}^{K-1}(\mu_{q}*F_{i})(y_{i})\\
&\qquad\qquad\qquad\qquad\times
\sum_{\substack{s_{i}\in\{0,\ldots,q-1\}^{d}\\0\le i\le K-1}}
e(\mathcal{Q}_{\theta}(y+s)).
\end{aligned} M q , a , θ ( G 0 , q , … , G K − 1 , q ) = E r mod q y ∈ ( Z d ) K y i ≡ r i ( mod q ) ( 0 ≤ i ≤ K − 1 ) ∑ e q ( Q a ( r )) i = 0 ∏ K − 1 ( μ q ∗ F i ) ( y i ) × s i ∈ { 0 , … , q − 1 } d 0 ≤ i ≤ K − 1 ∑ e ( Q θ ( y + s )) .
Expanding the convolutions gives
M q , a , θ ( G 0 , q , … , G K − 1 , q ) = 1 N q K d E r m o d q ∑ h i ∈ { 0 , … , N q − 1 } d 0 ≤ i ≤ K − 1 ∑ y ∈ ( Z d ) K y i ≡ r i ( m o d q ) ( 0 ≤ i ≤ K − 1 ) e q ( Q a ( r ) ) ∏ i = 0 K − 1 F i ( y i − q h i ) ∑ s i ∈ { 0 , … , q − 1 } d 0 ≤ i ≤ K − 1 e ( Q θ ( y + s ) ) . \begin{aligned}
\mathcal{M}_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})
={}&\frac{1}{N_{q}^{Kd}}\mathbb{E}_{r\ \mathrm{mod}\ q}
\sum_{\substack{h_{i}\in\{0,\ldots,N_{q}-1\}^{d}\\0\le i\le K-1}}
\sum_{\substack{y\in(\mathbb{Z}^{d})^{K}\\ y_{i}\equiv r_{i}\ (\mathrm{mod}\ q)\ (0\le i\le K-1)}}\\
&\qquad e_{q}(\mathcal{Q}_{a}(r))\prod_{i=0}^{K-1}F_{i}(y_{i}-qh_{i})
\sum_{\substack{s_{i}\in\{0,\ldots,q-1\}^{d}\\0\le i\le K-1}}
e(\mathcal{Q}_{\theta}(y+s)).
\end{aligned} M q , a , θ ( G 0 , q , … , G K − 1 , q ) = N q K d 1 E r mod q h i ∈ { 0 , … , N q − 1 } d 0 ≤ i ≤ K − 1 ∑ y ∈ ( Z d ) K y i ≡ r i ( mod q ) ( 0 ≤ i ≤ K − 1 ) ∑ e q ( Q a ( r )) i = 0 ∏ K − 1 F i ( y i − q h i ) s i ∈ { 0 , … , q − 1 } d 0 ≤ i ≤ K − 1 ∑ e ( Q θ ( y + s )) .
For fixed h = ( h 0 , … , h K − 1 ) h=(h_{0},\ldots,h_{K-1}) h = ( h 0 , … , h K − 1 ) , make the change of variables
z i : = y i − q h i ( 0 ≤ i ≤ K − 1 ) . z_{i}:=y_{i}-qh_{i}\qquad(0\le i\le K-1). z i := y i − q h i ( 0 ≤ i ≤ K − 1 ) .
Then z i ≡ r i ( m o d q ) z_{i}\equiv r_{i}\pmod q z i ≡ r i ( mod q ) , y + s = z + q h + s y+s=z+qh+s y + s = z + q h + s , and e q ( Q a ( r ) ) = e q ( Q a ( z ) ) e_{q}(\mathcal{Q}_{a}(r))=e_{q}(\mathcal{Q}_{a}(z)) e q ( Q a ( r )) = e q ( Q a ( z )) . For each z ∈ ( Z d ) K z\in(\mathbb{Z}^{d})^{K} z ∈ ( Z d ) K , there is exactly one r r r mod q q q satisfying z i ≡ r i ( m o d q ) z_{i}\equiv r_{i}\pmod q z i ≡ r i ( mod q ) for every i i i . Since the average over r r r is normalized, it contributes a factor q − K d q^{-Kd} q − K d . Therefore
M q , a , θ ( G 0 , q , … , G K − 1 , q ) = 1 ( q N q ) K d ∑ z ∈ ( Z d ) K e q ( Q a ( z ) ) ∏ i = 0 K − 1 F i ( z i ) × ∑ h i ∈ { 0 , … , N q − 1 } d s i ∈ { 0 , … , q − 1 } d 0 ≤ i ≤ K − 1 e ( Q θ ( z + q h + s ) ) . \begin{aligned}
\mathcal{M}_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})
={}&\frac{1}{(qN_{q})^{Kd}}
\sum_{z\in(\mathbb{Z}^{d})^{K}}e_{q}(\mathcal{Q}_{a}(z))\prod_{i=0}^{K-1}F_{i}(z_{i})\\
&\qquad\times
\sum_{\substack{h_{i}\in\{0,\ldots,N_{q}-1\}^{d}\\s_{i}\in\{0,\ldots,q-1\}^{d}\\0\le i\le K-1}}
e(\mathcal{Q}_{\theta}(z+qh+s)).
\end{aligned} M q , a , θ ( G 0 , q , … , G K − 1 , q ) = ( q N q ) K d 1 z ∈ ( Z d ) K ∑ e q ( Q a ( z )) i = 0 ∏ K − 1 F i ( z i ) × h i ∈ { 0 , … , N q − 1 } d s i ∈ { 0 , … , q − 1 } d 0 ≤ i ≤ K − 1 ∑ e ( Q θ ( z + q h + s )) .
Finally, for each i i i , the map ( h i , s i ) ↦ t i : = q h i + s i (h_i,s_i) \mapsto t_i := qh_i+s_i ( h i , s i ) ↦ t i := q h i + s i is a bijection from { 0 , … , N q − 1 } d × { 0 , … , q − 1 } d \{0,\ldots,N_q-1\}^d \times\{0,\ldots,q-1\}^d { 0 , … , N q − 1 } d × { 0 , … , q − 1 } d onto { 0 , … , q N q − 1 } d \{0,\ldots,qN_q-1\}^d { 0 , … , q N q − 1 } d .
Consequently,
M q , a , θ ( G 0 , q , … , G K − 1 , q ) = ∑ z ∈ ( Z d ) K e q ( Q a ( z ) ) ∏ i = 0 K − 1 F i ( z i ) × 1 ( q N q ) K d ∑ t i ∈ { 0 , … , q N q − 1 } d 0 ≤ i ≤ K − 1 e ( Q θ ( z + t ) ) . (63) M_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})=\sum_{z\in(\mathbb{Z}^d)^K}e_q(Q_a(z))\prod_{i=0}^{K-1}F_i(z_i)\times\frac{1}{(qN_q)^{Kd}}\sum_{\substack{t_i\in\{0,\ldots,qN_q-1\}^d\\0\leq i\leq K-1}}e(Q_\theta(z+t)). \tag*{(63)} M q , a , θ ( G 0 , q , … , G K − 1 , q ) = z ∈ ( Z d ) K ∑ e q ( Q a ( z )) i = 0 ∏ K − 1 F i ( z i ) × ( q N q ) K d 1 t i ∈ { 0 , … , q N q − 1 } d 0 ≤ i ≤ K − 1 ∑ e ( Q θ ( z + t )) . (63)
Comparing (62) and (63) , the rational phase and the values of the F i F_i F i are identical. The only difference is that e ( Q θ ( z ) ) e(Q_\theta(z)) e ( Q θ ( z )) is replaced by the average of e ( Q θ ( z + t ) ) e(Q_\theta(z+t)) e ( Q θ ( z + t )) over the indicated choices of t t t .
For 0 ≤ i ≤ K − 1 0\leq i\leq K-1 0 ≤ i ≤ K − 1 ,
∇ x i Q θ ( x ) = 2 ∑ j : { i , j } ∈ E θ i j ( x i − x j ) . \nabla_{x_i}Q_\theta(x)=2\sum_{j:\{i,j\}\in E}\theta_{ij}(x_i-x_j). ∇ x i Q θ ( x ) = 2 j : { i , j } ∈ E ∑ θ ij ( x i − x j ) .
If z i ∈ [ N ] d z_i\in[N]^d z i ∈ [ N ] d and t i ∈ { 0 , … , q N q − 1 } d t_i\in\{0,\ldots,qN_q-1\}^d t i ∈ { 0 , … , q N q − 1 } d for every i i i , then every point x x x on the segment joining z z z to z + t z+t z + t satisfies
max i , j ∣ x i − x j ∣ ≪ d N + q N q ≪ d N . \max_{i,j}|x_i-x_j|\ll_d N+qN_q\ll_d N. i , j max ∣ x i − x j ∣ ≪ d N + q N q ≪ d N .
Since ∥ θ ∥ ∞ ≤ Q / R 2 \|\theta\|_\infty\leq Q/R^2 ∥ θ ∥ ∞ ≤ Q / R 2 and q N q ≤ R Q − L ( L + 2 ) − 3 qN_q\leq RQ^{-L(L+2)-3} q N q ≤ R Q − L ( L + 2 ) − 3 , the mean value theorem gives
∣ e ( Q θ ( z ) ) − e ( Q θ ( z + t ) ) ∣ ≪ K , d Q N R 2 q N q ≪ K , d , σ Q − L ( L + 2 ) − 2 . \left|e(Q_\theta(z))-e(Q_\theta(z+t))\right|\ll_{K,d}\frac{QN}{R^2}qN_q\ll_{K,d,\sigma}Q^{-L(L+2)-2}. ∣ e ( Q θ ( z )) − e ( Q θ ( z + t )) ∣ ≪ K , d R 2 QN q N q ≪ K , d , σ Q − L ( L + 2 ) − 2 .
Here we used R = ⌊ σ N ⌋ R=\lfloor\sigma N\rfloor R = ⌊ σ N ⌋ and (56) , which imply N ≪ σ R N\ll_\sigma R N ≪ σ R . Averaging this estimate in t t t and using (62) --(63) gives
∣ S a / q + θ ( F 0 , … , F K − 1 ) − M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ ≪ K , d , σ Q − L ( L + 2 ) − 2 ∏ i = 0 K − 1 ∑ z i ∈ [ N ] d ∣ F i ( z i ) ∣ . (64) \left|S_{a/q+\theta}(F_0,\ldots,F_{K-1})-M_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})\right|\ll_{K,d,\sigma}Q^{-L(L+2)-2}\prod_{i=0}^{K-1}\sum_{z_i\in[N]^d}|F_i(z_i)|. \tag*{(64)} S a / q + θ ( F 0 , … , F K − 1 ) − M q , a , θ ( G 0 , q , … , G K − 1 , q ) ≪ K , d , σ Q − L ( L + 2 ) − 2 i = 0 ∏ K − 1 z i ∈ [ N ] d ∑ ∣ F i ( z i ) ∣. (64)
Cauchy--Schwarz in each factor proves (61) .
The complete sum
With normalized counting measure, define the complete cut operator
K q , a , d S , T : L 2 ( ( ( Z / q Z ) d ) T ) ⟶ L 2 ( ( ( Z / q Z ) d ) S ) , \mathcal{K}_{q,a,d}^{S,T}:L^2(((\mathbb{Z}/q\mathbb{Z})^d)^T)\longrightarrow L^2(((\mathbb{Z}/q\mathbb{Z})^d)^S), K q , a , d S , T : L 2 ((( Z / q Z ) d ) T ) ⟶ L 2 ((( Z / q Z ) d ) S ) ,
( K q , a , d S , T H ) ( r S ) : = E r T m o d q e q ( − 2 ∑ i ∈ S , j ∈ T a i j r i ⋅ r j ) H ( r T ) . (\mathcal{K}_{q,a,d}^{S,T}H)(r_S):=\mathbb{E}_{r_T\mathbin{\mathrm{mod}}q}e_q\left(-2\sum_{\substack{i\in S,j\in T}}a_{ij}r_i\cdot r_j\right)H(r_T). ( K q , a , d S , T H ) ( r S ) := E r T mod q e q − 2 i ∈ S , j ∈ T ∑ a ij r i ⋅ r j H ( r T ) .
We now compute the 2 → 2 2\to2 2 → 2 norm of this cut operator.
Lemma 7.3. One has
∥ K q , a , d S , T ∥ 2 → 2 = ∣ im ( 2 a S , T ) ∣ − d / 2 . (65) \|\mathcal{K}_{q,a,d}^{S,T}\|_{2\to2}=|\operatorname{im}(2a_{S,T})|^{-d/2}. \tag*{(65)} ∥ K q , a , d S , T ∥ 2 → 2 = ∣ im ( 2 a S , T ) ∣ − d /2 . (65)
Proof. By the same argument as in the proof of Lemma 5.2,
∥ K q , a , d S , T ∥ 2 → 2 = ∥ K q , a , 1 S , T ∥ 2 → 2 d . (66) \|\mathcal{K}_{q,a,d}^{S,T}\|_{2\to2}=\|\mathcal{K}_{q,a,1}^{S,T}\|_{2\to2}^{d}. \tag*{(66)} ∥ K q , a , d S , T ∥ 2 → 2 = ∥ K q , a , 1 S , T ∥ 2 → 2 d . (66)
It therefore remains to compute ∥ K q , a , 1 S , T ∥ 2 → 2 \|\mathcal{K}_{q,a,1}^{S,T}\|_{2\to2} ∥ K q , a , 1 S , T ∥ 2 → 2 . Write K : = K q , a , 1 S , T \mathcal{K}:=\mathcal{K}_{q,a,1}^{S,T} K := K q , a , 1 S , T . For a function f f f on ( Z / q Z ) S (\mathbb{Z}/q\mathbb{Z})^S ( Z / q Z ) S , expanding K K ∗ \mathcal{K}\mathcal{K}^* K K ∗ gives
( K K ∗ f ) ( r S ) = E r S ′ m o d q f ( r S ′ ) E r T m o d q e q ( ( r S ′ − r S ) T 2 a S , T r T ) = q − ∣ S ∣ ∑ r S ′ m o d q ( 2 a S , T ) T ( r S − r S ′ ) = 0 f ( r S ′ ) , (\mathcal{K}\mathcal{K}^*f)(r_S)=\mathbb{E}_{r'_S\ \mathrm{mod}\ q}f(r'_S)\mathbb{E}_{r_T\ \mathrm{mod}\ q}e_q\left((r'_S-r_S)^{\mathsf{T}}2a_{S,T}r_T\right)
= q^{-|S|}\sum_{\substack{r'_S\ \mathrm{mod}\ q\\(2a_{S,T})^{\mathsf{T}}(r_S-r'_S)=0}}f(r'_S), ( K K ∗ f ) ( r S ) = E r S ′ mod q f ( r S ′ ) E r T mod q e q ( ( r S ′ − r S ) T 2 a S , T r T ) = q − ∣ S ∣ r S ′ mod q ( 2 a S , T ) T ( r S − r S ′ ) = 0 ∑ f ( r S ′ ) ,
where the second equality follows from the orthogonality relation. Making the substitution h = r S − r S ′ h=r_S-r'_S h = r S − r S ′ gives
( K K ∗ f ) ( r S ) = q − ∣ S ∣ ∑ h ∈ ker ( ( 2 a S , T ) T ) f ( r S − h ) . (\mathcal{K}\mathcal{K}^*f)(r_S)=q^{-|S|}\sum_{h\in\ker((2a_{S,T})^{\mathsf{T}})}f(r_S-h). ( K K ∗ f ) ( r S ) = q − ∣ S ∣ h ∈ k e r (( 2 a S , T ) T ) ∑ f ( r S − h ) .
Hence, by Cauchy–Schwarz,
∥ K K ∗ f ∥ 2 2 = q − 2 ∣ S ∣ E r S m o d q ∣ ∑ h ∈ ker ( ( 2 a S , T ) T ) f ( r S − h ) ∣ 2 ≤ q − 2 ∣ S ∣ ∣ ker ( ( 2 a S , T ) T ) ∣ ∑ h ∈ ker ( ( 2 a S , T ) T ) E r S m o d q ∣ f ( r S − h ) ∣ 2 = ( ∣ ker ( ( 2 a S , T ) T ) ∣ q ∣ S ∣ ) 2 ∥ f ∥ 2 2 . \begin{aligned}
\|\mathcal{K}\mathcal{K}^*f\|_2^2
&=q^{-2|S|}\mathbb{E}_{r_S\ \mathrm{mod}\ q}\left|\sum_{h\in\ker((2a_{S,T})^{\mathsf{T}})}f(r_S-h)\right|^2\\
&\le q^{-2|S|}|\ker((2a_{S,T})^{\mathsf{T}})|\sum_{h\in\ker((2a_{S,T})^{\mathsf{T}})}\mathbb{E}_{r_S\ \mathrm{mod}\ q}|f(r_S-h)|^2\\
&=\left(\frac{|\ker((2a_{S,T})^{\mathsf{T}})|}{q^{|S|}}\right)^2\|f\|_2^2.
\end{aligned} ∥ K K ∗ f ∥ 2 2 = q − 2∣ S ∣ E r S mod q h ∈ k e r (( 2 a S , T ) T ) ∑ f ( r S − h ) 2 ≤ q − 2∣ S ∣ ∣ ker (( 2 a S , T ) T ) ∣ h ∈ k e r (( 2 a S , T ) T ) ∑ E r S mod q ∣ f ( r S − h ) ∣ 2 = ( q ∣ S ∣ ∣ ker (( 2 a S , T ) T ) ∣ ) 2 ∥ f ∥ 2 2 .
This bound is attained by f = 1 f=1 f = 1 , since K K ∗ 1 = ( ∣ ker ( ( 2 a S , T ) T ) ∣ / q ∣ S ∣ ) 1 \mathcal{K}\mathcal{K}^*1=(|\ker((2a_{S,T})^{\mathsf{T}})|/q^{|S|})1 K K ∗ 1 = ( ∣ ker (( 2 a S , T ) T ) ∣/ q ∣ S ∣ ) 1 . Thus
∥ K K ∗ ∥ 2 → 2 = ∣ ker ( ( 2 a S , T ) T ) ∣ q ∣ S ∣ . \|\mathcal{K}\mathcal{K}^*\|_{2\to2}=\frac{|\ker((2a_{S,T})^{\mathsf{T}})|}{q^{|S|}}. ∥ K K ∗ ∥ 2 → 2 = q ∣ S ∣ ∣ ker (( 2 a S , T ) T ) ∣ .
Finally, the orthogonality relation gives
∣ ker ( ( 2 a S , T ) T ) ∣ = ∑ r S m o d q E r T m o d q e q ( r S T 2 a S , T r T ) = q ∣ S ∣ − ∣ T ∣ ∣ ker ( 2 a S , T ) ∣ = q ∣ S ∣ ∣ im ( 2 a S , T ) ∣ , \begin{aligned}
|\ker((2a_{S,T})^{\mathsf{T}})|
&=\sum_{r_S\ \mathrm{mod}\ q}\mathbb{E}_{r_T\ \mathrm{mod}\ q}e_q(r_S^{\mathsf{T}}2a_{S,T}r_T)\\
&=q^{|S|-|T|}|\ker(2a_{S,T})|\\
&=\frac{q^{|S|}}{|\operatorname{im}(2a_{S,T})|},
\end{aligned} ∣ ker (( 2 a S , T ) T ) ∣ = r S mod q ∑ E r T mod q e q ( r S T 2 a S , T r T ) = q ∣ S ∣ − ∣ T ∣ ∣ ker ( 2 a S , T ) ∣ = ∣ im ( 2 a S , T ) ∣ q ∣ S ∣ ,
where the last equality follows from
q ∣ T ∣ = ∣ ker ( 2 a S , T ) ∣ ∣ im ( 2 a S , T ) ∣ . q^{|T|}=|\ker(2a_{S,T})||\operatorname{im}(2a_{S,T})|. q ∣ T ∣ = ∣ ker ( 2 a S , T ) ∣∣ im ( 2 a S , T ) ∣.
Consequently,
∥ K q , a , 1 S , T ∥ 2 → 2 2 = ∥ K K ∗ ∥ 2 → 2 = ∣ im ( 2 a S , T ) ∣ − 1 . (67) \|\mathcal{K}_{q,a,1}^{S,T}\|_{2\to2}^2=\|\mathcal{K}\mathcal{K}^*\|_{2\to2}=|\operatorname{im}(2a_{S,T})|^{-1}. \tag*{(67)} ∥ K q , a , 1 S , T ∥ 2 → 2 2 = ∥ K K ∗ ∥ 2 → 2 = ∣ im ( 2 a S , T ) ∣ − 1 . (67)
Combining (66) and (67) proves (65) .
We need an unrestricted matrix moment and a stronger moment when one entry is a unit.
Lemma 7.4 (Matrix moments). Let p p p be prime, and let m m m , s s s , t t t be positive integers. If τ > s + t − 1 \tau>s+t-1 τ > s + t − 1 , then, with normalized averaging over M ∈ Mat s × t ( Z / p m Z ) \mathbf{M}\in\operatorname{Mat}_{s\times t}(\mathbb{Z}/p^m\mathbb{Z}) M ∈ Mat s × t ( Z / p m Z ) , one has, for every fixed 1 ≤ i ≤ s 1\le i\le s 1 ≤ i ≤ s and 1 ≤ j ≤ t 1\le j\le t 1 ≤ j ≤ t ,
E M ∣ im ( 2 M ) ∣ − τ ≪ s , t , τ p − m s t , (68) \mathbb{E}_{\mathbf{M}}|\operatorname{im}(2\mathbf{M})|^{-\tau}\ll_{s,t,\tau}p^{-mst}, \tag*{(68)} E M ∣ im ( 2 M ) ∣ − τ ≪ s , t , τ p − m s t , (68)
E M [ 1 M i j ∈ ( Z / p m Z ) × ∣ im ( 2 M ) ∣ − τ ] ≪ s , t , τ p − m [ τ + ( s − 1 ) ( t − 1 ) ] . (69) \mathbb{E}_{\mathbf{M}}\left[1_{M_{ij}\in(\mathbb{Z}/p^m\mathbb{Z})^\times}|\operatorname{im}(2\mathbf{M})|^{-\tau}\right]\ll_{s,t,\tau}p^{-m[\tau+(s-1)(t-1)]}. \tag*{(69)} E M [ 1 M ij ∈ ( Z / p m Z ) × ∣ im ( 2 M ) ∣ − τ ] ≪ s , t , τ p − m [ τ + ( s − 1 ) ( t − 1 )] . (69)
The implicit constants depend only on s s s , t t t , and τ \tau τ .
Proof. Put
W s , t ( p m ) : = ∑ M ∈ Mat s × t ( Z / p m Z ) ∣ im M ∣ − τ , W_{s,t}(p^m) := \sum_{M \in\operatorname{Mat}_{s \times t}(\mathbb{Z}/p^m\mathbb{Z})} |\operatorname{im} M|^{-\tau}, W s , t ( p m ) := M ∈ Mat s × t ( Z / p m Z ) ∑ ∣ im M ∣ − τ ,
with the conventions W 0 , t ( p m ) = W s , 0 ( p m ) = W s , t ( 1 ) = 1 W_{0,t}(p^m) = W_{s,0}(p^m) = W_{s,t}(1) = 1 W 0 , t ( p m ) = W s , 0 ( p m ) = W s , t ( 1 ) = 1 .
The matrices divisible by p p p contribute W s , t ( p m − 1 ) W_{s,t}(p^{m-1}) W s , t ( p m − 1 ) . Every other matrix has a unit entry. Choose such an entry and use it as a pivot. After permuting rows and columns, write
M = ( u b c D ) , M = \begin{pmatrix} u & b \\ c & D \end{pmatrix}, M = ( u c b D ) ,
where b b b is a row vector and c c c is a column vector. Invertible row and column operations reduce M M M to
( u 0 0 M ′ ) , M ′ = D − c u − 1 b . \begin{pmatrix} u & 0 \\ 0 & M' \end{pmatrix}, \qquad M' = D - cu^{-1}b. ( u 0 0 M ′ ) , M ′ = D − c u − 1 b .
These operations preserve image cardinality, and hence ∣ im M ∣ = p m ∣ im M ′ ∣ |\operatorname{im} M| = p^m|\operatorname{im} M'| ∣ im M ∣ = p m ∣ im M ′ ∣ . There are O s , t ( p m ( s + t − 1 ) ) O_{s,t}(p^{m(s+t-1)}) O s , t ( p m ( s + t − 1 ) ) choices for the position of the pivot and for u u u , b b b , and c c c . For each such choice, the map C ⟼ M ′ = C − c u − 1 b C \longmapsto M' = C - cu^{-1}b C ⟼ M ′ = C − c u − 1 b is a bijection of Mat ( s − 1 ) × ( t − 1 ) ( Z / p m Z ) \operatorname{Mat}_{(s-1)\times(t-1)}(\mathbb{Z}/p^m\mathbb{Z}) Mat ( s − 1 ) × ( t − 1 ) ( Z / p m Z ) , so M ′ M' M ′ may be summed freely. Therefore
W s , t ( p m ) ≤ W s , t ( p m − 1 ) + c s , t p − m ( τ − s − t + 1 ) W s − 1 , t − 1 ( p m ) . W_{s,t}(p^m) \le W_{s,t}(p^{m-1}) + c_{s,t}p^{-m(\tau-s-t+1)}W_{s-1,t-1}(p^m). W s , t ( p m ) ≤ W s , t ( p m − 1 ) + c s , t p − m ( τ − s − t + 1 ) W s − 1 , t − 1 ( p m ) .
We now induct on min ( s , t ) \min(s,t) min ( s , t ) . The base case is min ( s , t ) = 0 \min(s,t) = 0 min ( s , t ) = 0 , when W s , t ( p m ) = 1 W_{s,t}(p^m) = 1 W s , t ( p m ) = 1 by convention. Suppose that s , t ≥ 1 s,t \ge1 s , t ≥ 1 , and assume inductively that W s − 1 , t − 1 ( p r ) ≪ s , t , τ 1 W_{s-1,t-1}(p^r) \ll_{s,t,\tau} 1 W s − 1 , t − 1 ( p r ) ≪ s , t , τ 1 uniformly in p p p and r r r . Set δ : = τ − s − t + 1 > 0 \delta:= \tau-s-t+1 > 0 δ := τ − s − t + 1 > 0 . Iterating the preceding recursion from m m m down to 0 0 0 gives
W s , t ( p m ) ≤ W s , t ( p m − 1 ) + c s , t p − m δ W s − 1 , t − 1 ( p m ) ≤ W s , t ( p m − 2 ) + c s , t p − ( m − 1 ) δ W s − 1 , t − 1 ( p m − 1 ) + c s , t p − m δ W s − 1 , t − 1 ( p m ) ≤ ⋮ ≤ W s , t ( 1 ) + c s , t ∑ r = 1 m p − r δ W s − 1 , t − 1 ( p r ) ≪ 1 + ∑ r = 1 m p − r δ ≪ 1. \begin{aligned}
W_{s,t}(p^m) &\le W_{s,t}(p^{m-1}) + c_{s,t}p^{-m\delta}W_{s-1,t-1}(p^m) \\
&\le W_{s,t}(p^{m-2}) + c_{s,t}p^{-(m-1)\delta}W_{s-1,t-1}(p^{m-1}) + c_{s,t}p^{-m\delta}W_{s-1,t-1}(p^m) \\
&\phantom{\le}\vdots\\
&\le W_{s,t}(1) + c_{s,t}\sum_{r=1}^{m}p^{-r\delta}W_{s-1,t-1}(p^r) \\
&\ll1 + \sum_{r=1}^{m}p^{-r\delta} \ll1.
\end{aligned} W s , t ( p m ) ≤ W s , t ( p m − 1 ) + c s , t p − m δ W s − 1 , t − 1 ( p m ) ≤ W s , t ( p m − 2 ) + c s , t p − ( m − 1 ) δ W s − 1 , t − 1 ( p m − 1 ) + c s , t p − m δ W s − 1 , t − 1 ( p m ) ≤ ⋮ ≤ W s , t ( 1 ) + c s , t r = 1 ∑ m p − r δ W s − 1 , t − 1 ( p r ) ≪ 1 + r = 1 ∑ m p − r δ ≪ 1.
Consequently,
E M ∣ im M ∣ − τ = p − m s t W s , t ( p m ) ≪ p − m s t . (70) \mathbb{E}_{M}|\operatorname{im} M|^{-\tau} = p^{-mst}W_{s,t}(p^m) \ll p^{-mst}. \tag*{(70)} E M ∣ im M ∣ − τ = p − m s t W s , t ( p m ) ≪ p − m s t . (70)
It remains to pass from M M M to 2 M 2M 2 M . If p p p is odd, multiplication by 2 2 2 is invertible modulo p m p^m p m , so ∣ im ( 2 M ) ∣ = ∣ im M ∣ |\operatorname{im}(2M)| = |\operatorname{im} M| ∣ im ( 2 M ) ∣ = ∣ im M ∣ . The estimate (70) therefore yields (68) .
Suppose now that p = 2 p=2 p = 2 and m ≥ 2 m\ge2 m ≥ 2 , and let M ‾ \overline{M} M denote the reduction of M M M modulo 2 m − 1 2^{m-1} 2 m − 1 . Then ∣ im ( 2 M m o d 2 m ) ∣ = ∣ im M ‾ ∣ |\operatorname{im}(2M \bmod2^m)| = |\operatorname{im}\overline{M}| ∣ im ( 2 M mod 2 m ) ∣ = ∣ im M ∣ . Every matrix modulo 2 m − 1 2^{m-1} 2 m − 1 has exactly 2 s t 2^{st} 2 s t lifts modulo 2 m 2^m 2 m . Hence, under normalized averaging, we have
E M m o d 2 m ∣ im ( 2 M ) ∣ − τ = E M ‾ m o d 2 m − 1 ∣ im M ‾ ∣ − τ ≪ 2 − ( m − 1 ) s t ≪ 2 − m s t . \mathbb{E}_{M \bmod2^m}|\operatorname{im}(2M)|^{-\tau}
=
\mathbb{E}_{\overline{M} \bmod2^{m-1}}|\operatorname{im}\overline{M}|^{-\tau}
\ll2^{-(m-1)st} \ll2^{-mst}. E M mod 2 m ∣ im ( 2 M ) ∣ − τ = E M mod 2 m − 1 ∣ im M ∣ − τ ≪ 2 − ( m − 1 ) s t ≪ 2 − m s t .
The constant lost in the final inequality depends only on s s s , t t t , and τ \tau τ . The case m = 1 m=1 m = 1 is immediate.
Finally, (69) follows immediately from the proof of (68) : use the prescribed unit entry M i j M_{ij} M ij as the pivot, and, when p = 2 p=2 p = 2 , note that reduction modulo 2 m − 1 2^{m-1} 2 m − 1 preserves whether an entry is a unit. ∎
For a ∈ ( Z / q Z ) L a \in(\mathbb{Z}/q\mathbb{Z})^L a ∈ ( Z / q Z ) L , define
s q ( a ) : = ∏ S ∣ T ∈ B K ∣ im ( 2 a S , T ) ∣ − τ w K . (71) s_q(a) := \prod_{S\mid T\in\mathcal{B}_K} \lvert\operatorname{im}(2a_{S,T})\rvert^{-\tau w_K}. \tag*{(71)} s q ( a ) := S ∣ T ∈ B K ∏ ∣ im ( 2 a S , T ) ∣ − τ w K . (71)
We use the exponent τ : = d ⌊ K 2 / 4 ⌋ / ( 2 L ) \tau:= d\lfloor K^2/4\rfloor/(2L) τ := d ⌊ K 2 /4 ⌋ / ( 2 L ) introduced in the proof of Proposition 6.1. Under the hypothesis d ≥ 4 K + 4 d \ge4K+4 d ≥ 4 K + 4 , that proof shows that τ > K + 2 \tau> K+2 τ > K + 2 . Then put η : = ( τ − K ) / 2 \eta:= (\tau-K)/2 η := ( τ − K ) /2 . Since τ > K + 2 \tau> K+2 τ > K + 2 , we have η > 1 \eta> 1 η > 1 .
Lemma 7.5. If d ≥ 4 K + 4 d \ge4K+4 d ≥ 4 K + 4 , then η > 1 \eta> 1 η > 1 and, for every q ≥ 1 q \ge1 q ≥ 1 ,
∑ a ∈ ( Z / q Z ) L ( a , q ) = 1 s q ( a ) ≪ K , d q − 1 − η . (72) \sum_{\substack{a\in(\mathbb{Z}/q\mathbb{Z})^L\\(a,q)=1}}s_q(a)\ll_{K,d}q^{-1-\eta}. \tag*{(72)} a ∈ ( Z / q Z ) L ( a , q ) = 1 ∑ s q ( a ) ≪ K , d q − 1 − η . (72)
Proof. The case q = 1 q=1 q = 1 is immediate. First let q = p m q=p^m q = p m . For each e ∈ E e\in\mathcal{E} e ∈ E , put
I e : = E a ∈ ( Z / p m Z ) L [ 1 a e ∈ ( Z / p m Z ) × s p m ( a ) ] . I_e:=\mathbb{E}_{a\in(\mathbb{Z}/p^m\mathbb{Z})^L}\left[1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}s_{p^m}(a)\right]. I e := E a ∈ ( Z / p m Z ) L [ 1 a e ∈ ( Z / p m Z ) × s p m ( a ) ] .
Fix e ∈ E e\in\mathcal{E} e ∈ E . In applying Lemma 5.5, take J = ∣ B K ∣ J=\lvert\mathcal{B}_K\rvert J = ∣ B K ∣ and label the indices 1 , … , J 1,\ldots,J 1 , … , J by the balanced cuts S ∣ T ∈ B K S\mid T\in\mathcal{B}_K S ∣ T ∈ B K . Thus, for the index j j j corresponding to S ∣ T S\mid T S ∣ T , the set E j \mathcal{E}_j E j in that lemma is the set of edges crossing S ∣ T S\mid T S ∣ T , and the weight c j c_j c j is w K w_K w K . We write f S , T f_{S,T} f S , T for the corresponding function f j f_j f j .
For each balanced cut S ∣ T S\mid T S ∣ T , define
f S , T ( a S , T ) : = { 1 a e ∈ ( Z / p m Z ) × ∣ im ( 2 a S , T ) ∣ − τ , e crosses S ∣ T , ∣ im ( 2 a S , T ) ∣ − τ , e does not cross S ∣ T . f_{S,T}(a_{S,T}):=
\begin{cases}
1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}\lvert\operatorname{im}(2a_{S,T})\rvert^{-\tau},& e\text{ crosses }S\mid T,\\
\lvert\operatorname{im}(2a_{S,T})\rvert^{-\tau},& e\text{ does not cross }S\mid T.
\end{cases} f S , T ( a S , T ) := { 1 a e ∈ ( Z / p m Z ) × ∣ im ( 2 a S , T ) ∣ − τ , ∣ im ( 2 a S , T ) ∣ − τ , e crosses S ∣ T , e does not cross S ∣ T .
When e e e crosses S ∣ T S\mid T S ∣ T , the coordinate a e a_e a e is one of the coordinates in a S , T a_{S,T} a S , T , so the first function is indeed defined on the product of the coordinate spaces indexed by E j \mathcal{E}_j E j .
For every e ′ ∈ E e'\in\mathcal{E} e ′ ∈ E , Lemma 5.4 gives
∑ S ∣ T ∈ B K e ′ crosses S ∣ T w K = 1 , \sum_{\substack{S\mid T\in\mathcal{B}_K\\e'\text{ crosses }S\mid T}}w_K=1, S ∣ T ∈ B K e ′ crosses S ∣ T ∑ w K = 1 ,
which is exactly the hypothesis of Lemma 5.5. Moreover,
∏ S ∣ T ∈ B K f S , T ( a S , T ) w K = ( ∏ S ∣ T ∈ B K e crosses S ∣ T 1 a e ∈ ( Z / p m Z ) × ) w K ∏ S ∣ T ∈ B K ∣ im ( 2 a S , T ) ∣ − τ w K = 1 a e ∈ ( Z / p m Z ) × s p m ( a ) , \prod_{S\mid T\in\mathcal{B}_K}f_{S,T}(a_{S,T})^{w_K}
=
\left(\prod_{\substack{S\mid T\in\mathcal{B}_K\\e\text{ crosses }S\mid T}}1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}\right)^{w_K}
\prod_{S\mid T\in\mathcal{B}_K}\lvert\operatorname{im}(2a_{S,T})\rvert^{-\tau w_K}
=
1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}s_{p^m}(a), S ∣ T ∈ B K ∏ f S , T ( a S , T ) w K = S ∣ T ∈ B K e crosses S ∣ T ∏ 1 a e ∈ ( Z / p m Z ) × w K S ∣ T ∈ B K ∏ ∣ im ( 2 a S , T ) ∣ − τ w K = 1 a e ∈ ( Z / p m Z ) × s p m ( a ) ,
where the last equality follows from (5.11). Hence
I e = E a ∈ ( Z / p m Z ) L ∏ S ∣ T ∈ B K f S , T ( a S , T ) w K . I_e=\mathbb{E}_{a\in(\mathbb{Z}/p^m\mathbb{Z})^L}\prod_{S\mid T\in\mathcal{B}_K}f_{S,T}(a_{S,T})^{w_K}. I e = E a ∈ ( Z / p m Z ) L S ∣ T ∈ B K ∏ f S , T ( a S , T ) w K .
Lemma 5.5 therefore gives
I e ≤ ∏ S ∣ T ∈ B K e crosses S ∣ T ( E a S , T [ 1 a e ∈ ( Z / p m Z ) × ∣ im ( 2 a S , T ) ∣ − τ ] ) w K ∏ S ∣ T ∈ B K e does not cross S ∣ T ( E a S , T ∣ im ( 2 a S , T ) ∣ − τ ) w K . I_e\le
\prod_{\substack{S\mid T\in\mathcal{B}_K\\e\text{ crosses }S\mid T}}
\left(\mathbb{E}_{a_{S,T}}\left[1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}\lvert\operatorname{im}(2a_{S,T})\rvert^{-\tau}\right]\right)^{w_K}
\prod_{\substack{S\mid T\in\mathcal{B}_K\\e\text{ does not cross }S\mid T}}
\left(\mathbb{E}_{a_{S,T}}\lvert\operatorname{im}(2a_{S,T})\rvert^{-\tau}\right)^{w_K}. I e ≤ S ∣ T ∈ B K e crosses S ∣ T ∏ ( E a S , T [ 1 a e ∈ ( Z / p m Z ) × ∣ im ( 2 a S , T ) ∣ − τ ] ) w K S ∣ T ∈ B K e does not cross S ∣ T ∏ ( E a S , T ∣ im ( 2 a S , T ) ∣ − τ ) w K .
where each average in a S , T a_{S,T} a S , T is normalized over Mat ∣ S ∣ × ∣ T ∣ ( Z / p m Z ) \operatorname{Mat}_{|S|\times|T|}(\mathbb{Z}/p^m\mathbb{Z}) Mat ∣ S ∣ × ∣ T ∣ ( Z / p m Z ) .
We now estimate the averages on the right. Fix a balanced cut S ∣ T S\mid T S ∣ T . If e e e crosses S ∣ T S\mid T S ∣ T , then (69) gives
E a S , T [ 1 a e ∈ ( Z / p m Z ) × ∣ im ( 2 a S , T ) ∣ − τ ] ≪ K , d p − m [ τ + ( ∣ S ∣ − 1 ) ( ∣ T ∣ − 1 ) ] = p − m ( ∣ S ∣ ∣ T ∣ + 1 + 2 η ) . \mathbb{E}_{a_{S,T}}\left[1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}|\operatorname{im}(2a_{S,T})|^{-\tau}\right]\ll_{K,d}p^{-m[\tau+(|S|-1)(|T|-1)]}=p^{-m(|S||T|+1+2\eta)}. E a S , T [ 1 a e ∈ ( Z / p m Z ) × ∣ im ( 2 a S , T ) ∣ − τ ] ≪ K , d p − m [ τ + ( ∣ S ∣ − 1 ) ( ∣ T ∣ − 1 )] = p − m ( ∣ S ∣∣ T ∣ + 1 + 2 η ) .
where we used ∣ S ∣ + ∣ T ∣ = K |S|+|T|=K ∣ S ∣ + ∣ T ∣ = K and 2 η = τ − K 2\eta=\tau-K 2 η = τ − K . If e e e does not cross S ∣ T S\mid T S ∣ T , then (68) gives
E a S , T ∣ im ( 2 a S , T ) ∣ − τ ≪ K , d p − m ∣ S ∣ ∣ T ∣ . \mathbb{E}_{a_{S,T}}|\operatorname{im}(2a_{S,T})|^{-\tau}\ll_{K,d}p^{-m|S||T|}. E a S , T ∣ im ( 2 a S , T ) ∣ − τ ≪ K , d p − m ∣ S ∣∣ T ∣ .
Consequently,
I e ≪ K , d p − m w K ∑ S ∣ T ∈ B K ∣ S ∣ ∣ T ∣ p − m ( 1 + 2 η ) w K # { S ∣ T ∈ B K : e crosses S ∣ T } . I_e\ll_{K,d}p^{-m w_K}\sum_{S\mid T\in\mathcal{B}_K}|S|^{|T|}p^{-m(1+2\eta)w_K}\#\{S\mid T\in\mathcal{B}_K:e\text{ crosses }S\mid T\}. I e ≪ K , d p − m w K S ∣ T ∈ B K ∑ ∣ S ∣ ∣ T ∣ p − m ( 1 + 2 η ) w K # { S ∣ T ∈ B K : e crosses S ∣ T } .
By (55) and (43) , I e ≪ K , d p − m ( L + 1 + 2 η ) I_e\ll_{K,d}p^{-m(L+1+2\eta)} I e ≪ K , d p − m ( L + 1 + 2 η ) .
If ( a , p m ) = 1 (a,p^m)=1 ( a , p m ) = 1 , then a e a_e a e is a unit for at least one e ∈ E e\in\mathcal{E} e ∈ E . Therefore
1 ( a , p m ) = 1 ≤ ∑ e ∈ E 1 a e ∈ ( Z / p m Z ) × . 1_{(a,p^m)=1}\leq\sum_{e\in\mathcal{E}}1_{a_e\in(\mathbb{Z}/p^m\mathbb{Z})^\times}. 1 ( a , p m ) = 1 ≤ e ∈ E ∑ 1 a e ∈ ( Z / p m Z ) × .
and hence
∑ a ∈ ( Z / p m Z ) L ( a , p m ) = 1 s p m ( a ) = p m L E a ∈ ( Z / p m Z ) L [ 1 ( a , p m ) = 1 s p m ( a ) ] ≤ p m L ∑ e ∈ E I e ≪ K , d p − m ( 1 + 2 η ) . \sum_{\substack{a\in(\mathbb{Z}/p^m\mathbb{Z})^L\\(a,p^m)=1}}\mathfrak{s}_{p^m}(a)
=p^{mL}\mathbb{E}_{a\in(\mathbb{Z}/p^m\mathbb{Z})^L}\left[1_{(a,p^m)=1}\mathfrak{s}_{p^m}(a)\right]
\leq p^{mL}\sum_{e\in\mathcal{E}}I_e\ll_{K,d}p^{-m(1+2\eta)}. a ∈ ( Z / p m Z ) L ( a , p m ) = 1 ∑ s p m ( a ) = p m L E a ∈ ( Z / p m Z ) L [ 1 ( a , p m ) = 1 s p m ( a ) ] ≤ p m L e ∈ E ∑ I e ≪ K , d p − m ( 1 + 2 η ) .
Now write q = ∏ r = 1 u p r m r q=\prod_{r=1}^u p_r^{m_r} q = ∏ r = 1 u p r m r . Under the Chinese remainder theorem, ( Z / q Z ) L ≅ ∏ r = 1 u ( Z / p r m r Z ) L (\mathbb{Z}/q\mathbb{Z})^L\cong\prod_{r=1}^u(\mathbb{Z}/p_r^{m_r}\mathbb{Z})^L ( Z / q Z ) L ≅ ∏ r = 1 u ( Z / p r m r Z ) L . Write a ( r ) a^{(r)} a ( r ) for the component of a a a modulo p r m r p_r^{m_r} p r m r . Then
( a , q ) = 1 ⟺ ( a ( r ) , p r m r ) = 1 for every 1 ≤ r ≤ u . (a,q)=1\Longleftrightarrow(a^{(r)},p_r^{m_r})=1\quad\text{for every }1\leq r\leq u. ( a , q ) = 1 ⟺ ( a ( r ) , p r m r ) = 1 for every 1 ≤ r ≤ u .
Moreover, for every balanced cut S ∣ T S\mid T S ∣ T , the map 2 a S , T 2a_{S,T} 2 a S , T corresponds under the Chinese remainder theorem to the product of the maps 2 a S , T ( r ) 2a^{(r)}_{S,T} 2 a S , T ( r ) . Therefore
∣ im ( 2 a S , T ) ∣ = ∏ r = 1 u ∣ im ( 2 a S , T ( r ) ) ∣ , |\operatorname{im}(2a_{S,T})|=\prod_{r=1}^u|\operatorname{im}(2a^{(r)}_{S,T})|, ∣ im ( 2 a S , T ) ∣ = r = 1 ∏ u ∣ im ( 2 a S , T ( r ) ) ∣ ,
and consequently s q ( a ) = ∏ r = 1 u s p r m r ( a ( r ) ) \mathfrak{s}_q(a)=\prod_{r=1}^u\mathfrak{s}_{p_r^{m_r}}(a^{(r)}) s q ( a ) = ∏ r = 1 u s p r m r ( a ( r ) ) . It follows that
∑ a ∈ ( Z / q Z ) L ( a , q ) = 1 s q ( a ) = ∏ r = 1 u ( ∑ a ( r ) ∈ ( Z / p r m r Z ) L ( a ( r ) , p r m r ) = 1 s p r m r ( a ( r ) ) ) ≪ K , d C ω ( q ) ∏ r = 1 u p r − m r ( 1 + 2 η ) = C ω ( q ) q − 1 − 2 η \sum_{\substack{a\in(\mathbb{Z}/q\mathbb{Z})^L\\(a,q)=1}}\mathfrak{s}_q(a)
=\prod_{r=1}^u\left(\sum_{\substack{a^{(r)}\in(\mathbb{Z}/p_r^{m_r}\mathbb{Z})^L\\(a^{(r)},p_r^{m_r})=1}}\mathfrak{s}_{p_r^{m_r}}(a^{(r)})\right)
\ll_{K,d}C^{\omega(q)}\prod_{r=1}^u p_r^{-m_r(1+2\eta)}
=C^{\omega(q)}q^{-1-2\eta} a ∈ ( Z / q Z ) L ( a , q ) = 1 ∑ s q ( a ) = r = 1 ∏ u a ( r ) ∈ ( Z / p r m r Z ) L ( a ( r ) , p r m r ) = 1 ∑ s p r m r ( a ( r ) ) ≪ K , d C ω ( q ) r = 1 ∏ u p r − m r ( 1 + 2 η ) = C ω ( q ) q − 1 − 2 η
for some C = C ( K , d ) C=C(K,d) C = C ( K , d ) . Since C ω ( q ) ≪ C , η q η C^{\omega(q)}\ll_{C,\eta}q^\eta C ω ( q ) ≪ C , η q η , this proves (72) .
We equip Z d × ( Z / q Z ) d \mathbb{Z}^{d} \times(\mathbb{Z}/q\mathbb{Z})^{d} Z d × ( Z / q Z ) d with counting measure in the first factor and normalized counting measure in the second. The estimates in this section give the following bound for the major arc form (59) .
Proposition 7.6. Assume d ≥ 4 K + 4 d \ge4K + 4 d ≥ 4 K + 4 , and let q ≥ 1 q \ge1 q ≥ 1 . Suppose that
G i ∈ L 2 ( Z d × ( Z / q Z ) d ) ( 0 ≤ i ≤ K − 1 ) . G_i \in L^2(\mathbb{Z}^{d} \times(\mathbb{Z}/q\mathbb{Z})^{d}) \qquad(0 \le i \le K - 1). G i ∈ L 2 ( Z d × ( Z / q Z ) d ) ( 0 ≤ i ≤ K − 1 ) .
For each i i i , suppose that
supp G i ⊆ [ P ] d × ( Z / q Z ) d . \operatorname{supp} G_i \subseteq[P]^d \times(\mathbb{Z}/q\mathbb{Z})^d. supp G i ⊆ [ P ] d × ( Z / q Z ) d .
Then
∑ a m o d q ( a , q ) = 1 ∫ T L ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ d θ ≪ K , d q − 1 − η P K d / 2 − 2 L ∏ i = 0 K − 1 ∥ G i ∥ 2 . (73) \sum_{\substack{a \bmod q \\ (a,q)=1}} \int_{\mathbb{T}^L} \left|\mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1})\right| \, \mathrm{d}\theta\ll_{K,d} q^{-1-\eta} P^{Kd/2-2L} \prod_{i=0}^{K-1} \|G_i\|_2. \tag*{(73)} a mod q ( a , q ) = 1 ∑ ∫ T L ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ d θ ≪ K , d q − 1 − η P K d /2 − 2 L i = 0 ∏ K − 1 ∥ G i ∥ 2 . (73)
Proof. Fix a balanced cut S ∣ T S \mid T S ∣ T . For each i i i , take X i = [ P ] d × ( Z / q Z ) d X_i = [P]^d \times(\mathbb{Z}/q\mathbb{Z})^d X i = [ P ] d × ( Z / q Z ) d . Use the three phase pieces
Q θ S ( u S , r S ) : = Q θ S ( u S ) + Q a S ( r S ) q , Q_{\theta}^{S}(u_S,r_S) := Q_{\theta}^{S}(u_S) + \frac{Q_{a}^{S}(r_S)}{q}, Q θ S ( u S , r S ) := Q θ S ( u S ) + q Q a S ( r S ) ,
Q θ S , T ( u S , r S , u T , r T ) : = Q θ S , T ( u S , u T ) + Q a S , T ( r S , r T ) q , Q_{\theta}^{S,T}(u_S,r_S,u_T,r_T) := Q_{\theta}^{S,T}(u_S,u_T) + \frac{Q_{a}^{S,T}(r_S,r_T)}{q}, Q θ S , T ( u S , r S , u T , r T ) := Q θ S , T ( u S , u T ) + q Q a S , T ( r S , r T ) ,
Q θ T ( u T , r T ) : = Q θ T ( u T ) + Q a T ( r T ) q . Q_{\theta}^{T}(u_T,r_T) := Q_{\theta}^{T}(u_T) + \frac{Q_{a}^{T}(r_T)}{q}. Q θ T ( u T , r T ) := Q θ T ( u T ) + q Q a T ( r T ) .
The cut operator in Lemma (33) is therefore
K S , T : L 2 ( ( [ P ] d × ( Z / q Z ) d ) T ) ⟶ L 2 ( ( [ P ] d × ( Z / q Z ) d ) S ) , \mathcal{K}^{S,T}: L^2\left(([P]^d \times(\mathbb{Z}/q\mathbb{Z})^d)^T\right) \longrightarrow L^2\left(([P]^d \times(\mathbb{Z}/q\mathbb{Z})^d)^S\right), K S , T : L 2 ( ([ P ] d × ( Z / q Z ) d ) T ) ⟶ L 2 ( ([ P ] d × ( Z / q Z ) d ) S ) ,
( K S , T H ) ( u S , r S ) : = E r T m o d q ∑ u T ∈ ( [ P ] d ) T e q ( Q a S , T ( r S , r T ) ) e ( Q θ S , T ( u S , u T ) ) H ( u T , r T ) . (\mathcal{K}^{S,T}H)(u_S,r_S) := \mathbb{E}_{r_T \bmod q} \sum_{u_T \in([P]^d)^T} e_q\left(Q_{a}^{S,T}(r_S,r_T)\right)e\left(Q_{\theta}^{S,T}(u_S,u_T)\right)H(u_T,r_T). ( K S , T H ) ( u S , r S ) := E r T mod q u T ∈ ([ P ] d ) T ∑ e q ( Q a S , T ( r S , r T ) ) e ( Q θ S , T ( u S , u T ) ) H ( u T , r T ) .
Thus
∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ ≤ ∥ K S , T ∥ 2 → 2 ∏ i = 0 K − 1 ∥ G i ∥ 2 . \left|\mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1})\right| \le\|\mathcal{K}^{S,T}\|_{2\to2} \prod_{i=0}^{K-1}\|G_i\|_2. ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ ≤ ∥ K S , T ∥ 2 → 2 i = 0 ∏ K − 1 ∥ G i ∥ 2 .
We now identify the norm of K S , T \mathcal{K}^{S,T} K S , T . The product structure of its kernel gives
K S , T = K P , d S , T ( − 2 θ S , T ) ⊗ K q , a , d S , T . (74) \mathcal{K}^{S,T} = \mathcal{K}_{P,d}^{S,T}(-2\theta_{S,T}) \otimes\mathcal{K}_{q,a,d}^{S,T}. \tag*{(74)} K S , T = K P , d S , T ( − 2 θ S , T ) ⊗ K q , a , d S , T . (74)
Hence (74) , multiplicativity of the operator norm under tensor products, and Lemmas (58) and (34) give
∥ K S , T ∥ 2 → 2 = ∥ K P , d S , T ( − 2 θ S , T ) ∥ 2 → 2 ∥ K q , a , d S , T ∥ 2 → 2 = P K d / 2 m P S , T ( − 2 θ S , T ) d ∣ im ( 2 a S , T ) ∣ − d / 2 . \begin{aligned}
\|\mathcal{K}^{S,T}\|_{2\to2} = \|\mathcal{K}_{P,d}^{S,T}(-2\theta_{S,T})\|_{2\to2}\|\mathcal{K}_{q,a,d}^{S,T}\|_{2\to2} \\
&= P^{Kd/2}m_{P}^{S,T}(-2\theta_{S,T})^{d}|\operatorname{im}(2a_{S,T})|^{-d/2}.
\end{aligned} ∥ K S , T ∥ 2 → 2 = ∥ K P , d S , T ( − 2 θ S , T ) ∥ 2 → 2 ∥ K q , a , d S , T ∥ 2 → 2 = P K d /2 m P S , T ( − 2 θ S , T ) d ∣ im ( 2 a S , T ) ∣ − d /2 .
Since d / ∣ B K ∣ = 2 τ w K d/|\mathcal{B}_K| = 2\tau w_K d /∣ B K ∣ = 2 τ w K , taking the geometric mean of these bounds over the balanced cuts gives
∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ ≤ P K d / 2 ∏ S ∣ T ∈ B K [ ∣ im ( 2 a S , T ) ∣ − 1 / 2 m P S , T ( − 2 θ S , T ) ] 2 τ w K ∏ i = 0 K − 1 ∥ G i ∥ 2 . |\mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1})| \le P^{Kd/2} \prod_{S\mid T\in\mathcal{B}_K} \left[|\operatorname{im}(2a_{S,T})|^{-1/2}m_{P}^{S,T}(-2\theta_{S,T})\right]^{2\tau w_K}\prod_{i=0}^{K-1}\|G_i\|_2. ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ ≤ P K d /2 S ∣ T ∈ B K ∏ [ ∣ im ( 2 a S , T ) ∣ − 1/2 m P S , T ( − 2 θ S , T ) ] 2 τ w K i = 0 ∏ K − 1 ∥ G i ∥ 2 .
By the definition (71) ,
∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ ≤ P K d / 2 s q ( a ) ∏ S ∣ T ∈ B K m P S , T ( − 2 θ S , T ) 2 τ w K ∏ i = 0 K − 1 ∥ G i ∥ 2 . |\mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1})| \le P^{Kd/2}s_q(a)\prod_{S\mid T\in\mathcal{B}_K}m_P^{S,T}(-2\theta_{S,T})^{2\tau w_K}\prod_{i=0}^{K-1}\lVert G_i\rVert_2. ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ ≤ P K d /2 s q ( a ) S ∣ T ∈ B K ∏ m P S , T ( − 2 θ S , T ) 2 τ w K i = 0 ∏ K − 1 ∥ G i ∥ 2 .
It remains to integrate the product of the m P S , T m_P^{S,T} m P S , T -terms. In applying Lemma 5.5, take J = ∣ B K ∣ J=|\mathcal{B}_K| J = ∣ B K ∣ and label the indices 1 , … , J 1,\ldots,J 1 , … , J by the balanced cuts S ∣ T ∈ B K S\mid T\in\mathcal{B}_K S ∣ T ∈ B K . Thus, for the index j j j corresponding to S ∣ T S\mid T S ∣ T , the set E j \mathcal{E}_j E j in that lemma is the set of edges crossing S ∣ T S\mid T S ∣ T , and the weight c j c_j c j is w K w_K w K . We write f S , T f_{S,T} f S , T for the corresponding function f j f_j f j and set
f S , T ( θ S , T ) : = m P S , T ( − 2 θ S , T ) 2 τ . f_{S,T}(\theta_{S,T}):=m_P^{S,T}(-2\theta_{S,T})^{2\tau}. f S , T ( θ S , T ) := m P S , T ( − 2 θ S , T ) 2 τ .
For every e ∈ E e\in\mathcal{E} e ∈ E , Lemma 5.4 gives
∑ S ∣ T ∈ B K e crosses S ∣ T w K = 1 , \sum_{\substack{S\mid T\in\mathcal{B}_K\\ e\text{ crosses }S\mid T}}w_K=1, S ∣ T ∈ B K e crosses S ∣ T ∑ w K = 1 ,
which is exactly the hypothesis of Lemma 5.5. Moreover,
∏ S ∣ T ∈ B K f S , T ( θ S , T ) w K = ∏ S ∣ T ∈ B K m P S , T ( − 2 θ S , T ) 2 τ w K . \prod_{S\mid T\in\mathcal{B}_K}f_{S,T}(\theta_{S,T})^{w_K}
=\prod_{S\mid T\in\mathcal{B}_K}m_P^{S,T}(-2\theta_{S,T})^{2\tau w_K}. S ∣ T ∈ B K ∏ f S , T ( θ S , T ) w K = S ∣ T ∈ B K ∏ m P S , T ( − 2 θ S , T ) 2 τ w K .
Lemma 5.5 therefore gives
∫ T L ∏ S ∣ T ∈ B K m P S , T ( − 2 θ S , T ) 2 τ w K d θ ≤ ∏ S ∣ T ∈ B K ( ∫ T ∣ S ∣ ∣ T ∣ m P S , T ( − 2 θ S , T ) 2 τ d θ S , T ) w K . \int_{\mathbb{T}^{L}}\prod_{S\mid T\in\mathcal{B}_K}m_P^{S,T}(-2\theta_{S,T})^{2\tau w_K}\,d\theta
\le
\prod_{S\mid T\in\mathcal{B}_K}
\left(\int_{\mathbb{T}^{|S||T|}}m_P^{S,T}(-2\theta_{S,T})^{2\tau}\,d\theta_{S,T}\right)^{w_K}. ∫ T L S ∣ T ∈ B K ∏ m P S , T ( − 2 θ S , T ) 2 τ w K d θ ≤ S ∣ T ∈ B K ∏ ( ∫ T ∣ S ∣∣ T ∣ m P S , T ( − 2 θ S , T ) 2 τ d θ S , T ) w K .
Multiplication by − 2 -2 − 2 preserves normalized Haar measure on T ∣ S ∣ ∣ T ∣ \mathbb{T}^{|S||T|} T ∣ S ∣∣ T ∣ . Since 0 ≤ m P S , T ≤ 1 0\le m_P^{S,T}\le1 0 ≤ m P S , T ≤ 1 and 2 τ > 2 K + 4 2\tau>2K+4 2 τ > 2 K + 4 , Lemma 2.1 gives
∫ T ∣ S ∣ ∣ T ∣ m P S , T ( − 2 θ S , T ) 2 τ d θ S , T ≪ K P − 2 ∣ S ∣ ∣ T ∣ . \int_{\mathbb{T}^{|S||T|}}m_P^{S,T}(-2\theta_{S,T})^{2\tau}\,d\theta_{S,T}\ll_K P^{-2|S||T|}. ∫ T ∣ S ∣∣ T ∣ m P S , T ( − 2 θ S , T ) 2 τ d θ S , T ≪ K P − 2∣ S ∣∣ T ∣ .
Using (55) , we therefore obtain
∫ T L ∏ S ∣ T ∈ B K m P S , T ( − 2 θ S , T ) 2 τ w K d θ ≪ K P − 2 L . \int_{\mathbb{T}^{L}}\prod_{S\mid T\in\mathcal{B}_K}m_P^{S,T}(-2\theta_{S,T})^{2\tau w_K}\,d\theta\ll_K P^{-2L}. ∫ T L S ∣ T ∈ B K ∏ m P S , T ( − 2 θ S , T ) 2 τ w K d θ ≪ K P − 2 L .
Summing over a a a using Lemma 7.5 and applying this estimate gives
∑ a m o d q ( a , q ) = 1 ∫ T L ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ d θ ≪ K , d q − 1 − η P K d / 2 − 2 L ∏ i = 0 K − 1 ∥ G i ∥ 2 . \sum_{\substack{a\bmod q\\(a,q)=1}}\int_{\mathbb{T}^{L}}|\mathcal{M}_{q,a,\theta}(G_0,\ldots,G_{K-1})|\,d\theta
\ll_{K,d}q^{-1-\eta}P^{Kd/2-2L}\prod_{i=0}^{K-1}\lVert G_i\rVert_2. a mod q ( a , q ) = 1 ∑ ∫ T L ∣ M q , a , θ ( G 0 , … , G K − 1 ) ∣ d θ ≪ K , d q − 1 − η P K d /2 − 2 L i = 0 ∏ K − 1 ∥ G i ∥ 2 .
This is (73) .
Proof of the major arc estimate
We can now prove the major arc estimate stated at the beginning of the section. It will simplify the argument to cover M Q \mathfrak{M}_Q M Q by boxes centered at rational points a / q a/q a / q , with a single denominator q q q shared by all coordinates. For 1 ≤ q ≤ 2 Q L 1\le q\le2Q^L 1 ≤ q ≤ 2 Q L and a ∈ ( Z / q Z ) L a\in(\mathbb{Z}/q\mathbb{Z})^L a ∈ ( Z / q Z ) L with ( a , q ) = 1 (a,q)=1 ( a , q ) = 1 , put
B Q ( a / q ) : = ∏ e ∈ E { β e ∈ T : ∥ β e − a e / q ∥ T ≤ Q R 2 } . \mathcal{B}_Q(a/q):=\prod_{e\in\mathcal{E}}\left\{\beta_e\in\mathbb{T}:\|\beta_e-a_e/q\|_{\mathbb{T}}\le\frac{Q}{R^2}\right\}. B Q ( a / q ) := e ∈ E ∏ { β e ∈ T : ∥ β e − a e / q ∥ T ≤ R 2 Q } .
Lemma 7.7. Assume 2 ≤ Q ≤ R 2 \le Q \le R 2 ≤ Q ≤ R . Then
M Q ⊆ ⋃ 1 ≤ q ≤ 2 Q L ⋃ a ∈ ( Z / q Z ) L ( a , q ) = 1 B Q ( a / q ) . (75) \mathcal{M}_Q \subseteq\bigcup_{1 \le q \le2Q^L} \ \bigcup_{\substack{a \in(\mathbb{Z}/q\mathbb{Z})^L \\ (a,q)=1}} \mathcal{B}_Q(a/q). \tag*{(75)} M Q ⊆ 1 ≤ q ≤ 2 Q L ⋃ a ∈ ( Z / q Z ) L ( a , q ) = 1 ⋃ B Q ( a / q ) . (75)
Proof. Fix β ∈ M Q \beta\in\mathcal{M}_Q β ∈ M Q . For every e ∈ E e \in\mathcal{E} e ∈ E , choose 1 ≤ q e ≤ Q 1 \le q_e \le Q 1 ≤ q e ≤ Q and b e ∈ Z / q e Z b_e \in\mathbb{Z}/q_e\mathbb{Z} b e ∈ Z / q e Z , with ( b e , q e ) = 1 (b_e,q_e)=1 ( b e , q e ) = 1 , such that
∥ − 2 β e − b e / q e ∥ T ≤ Q q e R 2 . \lVert-2\beta_e-b_e/q_e\rVert_{\mathbb{T}} \le\frac{Q}{q_eR^2}. ∥ − 2 β e − b e / q e ∥ T ≤ q e R 2 Q .
The two preimages of b e / q e b_e/q_e b e / q e under x ↦ − 2 x x \mapsto-2x x ↦ − 2 x are − b e / ( 2 q e ) -b_e/(2q_e) − b e / ( 2 q e ) and ( q e − b e ) / ( 2 q e ) (q_e-b_e)/(2q_e) ( q e − b e ) / ( 2 q e ) . Choose the one nearer to β e \beta_e β e and denote it by ρ e \rho_e ρ e . Then − 2 ρ e = b e / q e ∈ T -2\rho_e=b_e/q_e \in\mathbb{T} − 2 ρ e = b e / q e ∈ T and
∥ β e − ρ e ∥ T = 1 2 ∥ − 2 β e − b e / q e ∥ T ≤ Q 2 q e R 2 . \lVert\beta_e-\rho_e\rVert_{\mathbb{T}}=\frac{1}{2}\lVert-2\beta_e-b_e/q_e\rVert_{\mathbb{T}} \le\frac{Q}{2q_eR^2}. ∥ β e − ρ e ∥ T = 2 1 ∥ − 2 β e − b e / q e ∥ T ≤ 2 q e R 2 Q .
Notice that the ρ e \rho_e ρ e divides 2 q e 2q_e 2 q e .
Put ρ : = ( ρ e ) e ∈ E \rho:= (\rho_e)_{e\in\mathcal{E}} ρ := ( ρ e ) e ∈ E , and let q q q be the least common denominator of its coordinates. Then
q ∣ lcm e ∈ E ( 2 q e ) = 2 lcm e ∈ E q e , q \mid\operatorname{lcm}_{e\in\mathcal{E}}(2q_e)=2\operatorname{lcm}_{e\in\mathcal{E}}q_e, q ∣ lcm e ∈ E ( 2 q e ) = 2 lcm e ∈ E q e ,
and hence
q ≤ 2 ∏ e ∈ E q e ≤ 2 Q L . q \le2\prod_{e\in\mathcal{E}}q_e \le2Q^L. q ≤ 2 e ∈ E ∏ q e ≤ 2 Q L .
Write ρ = a / q \rho=a/q ρ = a / q with a ∈ ( Z / q Z ) L a \in(\mathbb{Z}/q\mathbb{Z})^L a ∈ ( Z / q Z ) L . Since q q q is the least common denominator, ( a , q ) = 1 (a,q)=1 ( a , q ) = 1 . Finally, for every e ∈ E e \in\mathcal{E} e ∈ E ,
∥ β e − a e / q ∥ T = ∥ β e − ρ e ∥ T ≤ Q 2 q e R 2 ≤ Q R 2 . \lVert\beta_e-a_e/q\rVert_{\mathbb{T}}=\lVert\beta_e-\rho_e\rVert_{\mathbb{T}} \le\frac{Q}{2q_eR^2} \le\frac{Q}{R^2}. ∥ β e − a e / q ∥ T = ∥ β e − ρ e ∥ T ≤ 2 q e R 2 Q ≤ R 2 Q .
Therefore β ∈ B Q ( a / q ) \beta\in\mathcal{B}_Q(a/q) β ∈ B Q ( a / q ) , proving (75) .
Proof of Proposition 7.1. By definition,
Λ N M Q ( F 0 , … , F K − 1 ) = ∫ M Q w ^ R ( Q β ( v ) ) S β ( F 0 , … , F K − 1 ) d β . \Lambda_N^{\mathcal{M}_Q}(F_0,\ldots,F_{K-1})=\int_{\mathcal{M}_Q}\widehat{w}_R(\mathcal{Q}_\beta(v))\mathcal{S}_\beta(F_0,\ldots,F_{K-1})\,d\beta. Λ N M Q ( F 0 , … , F K − 1 ) = ∫ M Q w R ( Q β ( v )) S β ( F 0 , … , F K − 1 ) d β .
Lemma 7.7, the triangle inequality, and (32) therefore give
∣ Λ N M Q ( F 0 , … , F K − 1 ) ∣ ≪ Δ ∑ 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) L ( a , q ) = 1 ∫ [ − Q / R 2 , Q / R 2 ] L ∣ S a / q + θ ( F 0 , … , F K − 1 ) ∣ d θ . (76) \left|\Lambda_N^{\mathcal{M}_Q}(F_0,\ldots,F_{K-1})\right|\ll_\Delta\sum_{1\le q\le2Q^L}\ \sum_{\substack{a\in(\mathbb{Z}/q\mathbb{Z})^L\\(a,q)=1}}\int_{[-Q/R^2,Q/R^2]^L}\left|\mathcal{S}_{a/q+\theta}(F_0,\ldots,F_{K-1})\right|\,d\theta. \tag*{(76)} Λ N M Q ( F 0 , … , F K − 1 ) ≪ Δ 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) L ( a , q ) = 1 ∑ ∫ [ − Q / R 2 , Q / R 2 ] L S a / q + θ ( F 0 , … , F K − 1 ) d θ . (76)
For every q q q , a a a , and θ \theta θ occurring in (76) , put
E q , a , θ : = S a / q + θ ( F 0 , … , F K − 1 ) − M q , a , θ ( G 0 , q , … , G K − 1 , q ) . E_{q,a,\theta}:=\mathcal{S}_{a/q+\theta}(F_0,\ldots,F_{K-1})-\mathcal{M}_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q}). E q , a , θ := S a / q + θ ( F 0 , … , F K − 1 ) − M q , a , θ ( G 0 , q , … , G K − 1 , q ) .
Lemma 7.2 gives
∣ E q , a , θ ∣ ≪ K , d , σ Q − L ( L + 2 ) − 2 N K d / 2 ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (77) |E_{q,a,\theta}|\ll_{K,d,\sigma}Q^{-L(L+2)-2}N^{Kd/2}\prod_{i=0}^{K-1}\|F_i\|_{\ell^2(\mathbb{Z}^d)}. \tag*{(77)} ∣ E q , a , θ ∣ ≪ K , d , σ Q − L ( L + 2 ) − 2 N K d /2 i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (77)
Put
P q : = N + q N q + q . (78) P_q:=N+qN_q+q. \tag*{(78)} P q := N + q N q + q . (78)
Then
supp G i , q ⊆ [ P q ] d × ( Z / q Z ) d . \operatorname{supp} G_{i,q} \subseteq[P_q]^d \times(\mathbb{Z}/q\mathbb{Z})^d. supp G i , q ⊆ [ P q ] d × ( Z / q Z ) d .
Indeed, if G i , q ( u , r ) ≠ 0 G_{i,q}(u,r) \ne0 G i , q ( u , r ) = 0 , then (60) and the definition of μ q \mu_q μ q give z ∈ [ N ] d z \in[N]^d z ∈ [ N ] d , h ∈ { 0 , … , N q − 1 } d h \in\{0,\ldots,N_q-1\}^d h ∈ { 0 , … , N q − 1 } d , and s ∈ { 0 , … , q − 1 } d s \in\{0,\ldots,q-1\}^d s ∈ { 0 , … , q − 1 } d such that
F i ( z ) ≠ 0 , u = z + q h + s . F_i(z) \ne0,\qquad u=z+qh+s. F i ( z ) = 0 , u = z + q h + s .
Proposition (61) therefore gives
∑ a ∈ ( Z / q Z ) d ( a , q ) = 1 ∫ [ − Q / R 2 , Q / R 2 ] L ∣ M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ d θ ≤ ∑ a mod q ( a , q ) = 1 ∫ T L ∣ M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ d θ \sum_{\substack{a\in(\mathbb{Z}/q\mathbb{Z})^d\\(a,q)=1}}\int_{[-Q/R^2,Q/R^2]^L} \left|M_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})\right|\,\mathrm{d}\theta
\leq
\sum_{\substack{a\operatorname{mod}q\\(a,q)=1}}\int_{\mathbb{T}^L} \left|M_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})\right|\,\mathrm{d}\theta a ∈ ( Z / q Z ) d ( a , q ) = 1 ∑ ∫ [ − Q / R 2 , Q / R 2 ] L ∣ M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ d θ ≤ a mod q ( a , q ) = 1 ∑ ∫ T L ∣ M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ d θ
≪ K , d q − 1 − η P q K d / 2 − 2 L ∏ i = 0 K − 1 ∥ G i , q ∥ 2 . (79) \ll_{K,d} q^{-1-\eta}P_q^{Kd/2-2L}\prod_{i=0}^{K-1}\|G_{i,q}\|_2. \tag*{(79)} ≪ K , d q − 1 − η P q K d /2 − 2 L i = 0 ∏ K − 1 ∥ G i , q ∥ 2 . (79)
Observe that
∥ G i , q ∥ 2 ≤ P q d ( 1 / 2 − 1 / K ) ∥ G i , q ∥ K . (80) \|G_{i,q}\|_2\leq P_q^{d(1/2-1/K)}\|G_{i,q}\|_K. \tag*{(80)} ∥ G i , q ∥ 2 ≤ P q d ( 1/2 − 1/ K ) ∥ G i , q ∥ K . (80)
For fixed u ∈ Z d u\in\mathbb{Z}^d u ∈ Z d , let y = y ( u , r ) y=y(u,r) y = y ( u , r ) be the point in (60) . The map r ⟼ s : = u − y ( u , r ) r\longmapsto s:=u-y(u,r) r ⟼ s := u − y ( u , r ) is a bijection from ( Z / q Z ) d (\mathbb{Z}/q\mathbb{Z})^d ( Z / q Z ) d onto { 0 , … , q − 1 } d \{0,\ldots,q-1\}^d { 0 , … , q − 1 } d . Therefore
∥ G i , q ∥ K K = ∑ u ∈ Z d E r mod q ∣ G i , q ( u , r ) ∣ K = 1 q d ∑ s ∈ { 0 , … , q − 1 } d ∑ u ∈ Z d ∣ ( μ q ∗ F i ) ( u − s ) ∣ K = ∥ μ q ∗ F i ∥ ℓ K ( Z d ) K . (81) \begin{aligned}
\|G_{i,q}\|_K^K=\sum_{u\in\mathbb{Z}^d}\mathbb{E}_{r\operatorname{mod}q}|G_{i,q}(u,r)|^K \\
&=\frac{1}{q^d}\sum_{s\in\{0,\ldots,q-1\}^d}\sum_{u\in\mathbb{Z}^d}|(\mu_q*F_i)(u-s)|^K \\
&=\|\mu_q*F_i\|_{\ell^K(\mathbb{Z}^d)}^K. \tag*{(81)}
\end{aligned} ∥ G i , q ∥ K K = u ∈ Z d ∑ E r mod q ∣ G i , q ( u , r ) ∣ K = q d 1 s ∈ { 0 , … , q − 1 } d ∑ u ∈ Z d ∑ ∣ ( μ q ∗ F i ) ( u − s ) ∣ K = ∥ μ q ∗ F i ∥ ℓ K ( Z d ) K . (81)
Moreover,
q N q ≤ R , q ≤ 2 Q L ≤ R , N ≤ 2 σ − 1 R . qN_q\leq R,\qquad q\leq2Q^L\leq R,\qquad N\leq2\sigma^{-1}R. q N q ≤ R , q ≤ 2 Q L ≤ R , N ≤ 2 σ − 1 R .
Here the first inequality follows from (57) , the second from (56) , and the third from R = ⌊ σ N ⌋ R=\lfloor\sigma N\rfloor R = ⌊ σ N ⌋ and N ≥ 2 / σ N\geq2/\sigma N ≥ 2/ σ . Thus P q ≪ σ R P_q\ll_\sigma R P q ≪ σ R . Equations (80) and (81) give
P q K d / 2 − 2 L ∏ i = 0 K − 1 ∥ G i , q ∥ 2 ≤ P q ( K − 1 ) d − 2 L ∏ i = 0 K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) . P_q^{Kd/2-2L}\prod_{i=0}^{K-1}\|G_{i,q}\|_2
\leq P_q^{(K-1)d-2L}\prod_{i=0}^{K-1}\|\mu_q*F_i\|_{\ell^K(\mathbb{Z}^d)}. P q K d /2 − 2 L i = 0 ∏ K − 1 ∥ G i , q ∥ 2 ≤ P q ( K − 1 ) d − 2 L i = 0 ∏ K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) .
Consequently, summing (79) over q ≤ 2 Q L q\leq2Q^L q ≤ 2 Q L gives
∑ 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) d ( a , q ) = 1 ∫ [ − Q / R 2 , Q / R 2 ] L ∣ M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ d θ ≪ K , d , σ R ( K − 1 ) d − 2 L ∑ q ≤ 2 Q L q − 1 − η ∏ i = 0 K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) . (82) \sum_{1\leq q\leq2Q^L}\sum_{\substack{a\in(\mathbb{Z}/q\mathbb{Z})^d\\(a,q)=1}}\int_{[-Q/R^2,Q/R^2]^L}\left|M_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})\right|\,\mathrm{d}\theta
\ll_{K,d,\sigma}R^{(K-1)d-2L}\sum_{q\leq2Q^L}q^{-1-\eta}\prod_{i=0}^{K-1}\|\mu_q*F_i\|_{\ell^K(\mathbb{Z}^d)}. \tag*{(82)} 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) d ( a , q ) = 1 ∑ ∫ [ − Q / R 2 , Q / R 2 ] L ∣ M q , a , θ ( G 0 , q , … , G K − 1 , q ) ∣ d θ ≪ K , d , σ R ( K − 1 ) d − 2 L q ≤ 2 Q L ∑ q − 1 − η i = 0 ∏ K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) . (82)
For each q q q ,
# { a ∈ ( Z / q Z ) L : ( a , q ) = 1 } ≤ q L , \#\{a\in(\mathbb{Z}/q\mathbb{Z})^L:(a,q)=1\}\leq q^L, # { a ∈ ( Z / q Z ) L : ( a , q ) = 1 } ≤ q L ,
and the measure of [ − Q / R 2 , Q / R 2 ] L [-Q/R^2,Q/R^2]^L [ − Q / R 2 , Q / R 2 ] L is ( 2 Q / R 2 ) L (2Q/R^2)^L ( 2 Q / R 2 ) L . Hence
∑ 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) L ( a , q ) = 1 ∫ [ − Q / R 2 , Q / R 2 ] L d θ ≤ ( 2 Q R 2 ) L ∑ q ≤ 2 Q L q L ≪ L Q L ( L + 2 ) R − 2 L . \sum_{1\leq q\leq2Q^L}\sum_{\substack{a\in(\mathbb{Z}/q\mathbb{Z})^L\\(a,q)=1}}\int_{[-Q/R^2,Q/R^2]^L}\mathrm{d}\theta
\leq\left(\frac{2Q}{R^2}\right)^L\sum_{q\leq2Q^L}q^L
\ll_L Q^{L(L+2)}R^{-2L}. 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) L ( a , q ) = 1 ∑ ∫ [ − Q / R 2 , Q / R 2 ] L d θ ≤ ( R 2 2 Q ) L q ≤ 2 Q L ∑ q L ≪ L Q L ( L + 2 ) R − 2 L .
It follows from (77) that
∑ 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) ( a , q ) = 1 ∫ [ − Q / R 2 , Q / R 2 ] L ∣ E q , a , θ ∣ d θ ≪ K , d , σ Q − L ( L + 2 ) − 2 N K d / 2 ⋅ Q L ( L + 2 ) R − 2 L ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) \sum_{1 \leq q \leq2Q^{L}} \sum_{\substack{a \in(\mathbb{Z}/q\mathbb{Z})\\(a,q)=1}} \int_{[-Q/R^{2},Q/R^{2}]^{L}} \lvert E_{q,a,\theta}\rvert\, \mathrm{d}\theta\ll_{K,d,\sigma} Q^{-L(L+2)-2}N^{Kd/2}\cdot Q^{L(L+2)}R^{-2L}\prod_{i=0}^{K-1}\lVert F_i\rVert_{\ell^{2}(\mathbb{Z}^{d})} 1 ≤ q ≤ 2 Q L ∑ a ∈ ( Z / q Z ) ( a , q ) = 1 ∑ ∫ [ − Q / R 2 , Q / R 2 ] L ∣ E q , a , θ ∣ d θ ≪ K , d , σ Q − L ( L + 2 ) − 2 N K d /2 ⋅ Q L ( L + 2 ) R − 2 L i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d )
≪ K , d , σ Q − 2 R K d / 2 − 2 L ∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (83) \ll_{K,d,\sigma} Q^{-2}R^{Kd/2-2L}\prod_{i=0}^{K-1}\lVert F_i\rVert_{\ell^{2}(\mathbb{Z}^{d})}. \tag*{(83)} ≪ K , d , σ Q − 2 R K d /2 − 2 L i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) . (83)
Since
S a / q + θ ( F 0 , … , F K − 1 ) = M q , a , θ ( G 0 , q , … , G K − 1 , q ) + E q , a , θ , S_{a/q+\theta}(F_0,\ldots,F_{K-1})=\mathcal{M}_{q,a,\theta}(G_{0,q},\ldots,G_{K-1,q})+E_{q,a,\theta}, S a / q + θ ( F 0 , … , F K − 1 ) = M q , a , θ ( G 0 , q , … , G K − 1 , q ) + E q , a , θ ,
the bounds (76) , (82) , and (83) prove (58) . □
The first term in (58) retains the averages μ q ∗ F i \mu_q * F_i μ q ∗ F i , which are used in the density-increment argument of the next section. After decreasing the exponent in Proposition 6.1 if necessary, assume from now on that 0 < γ ≤ 1 0 < \gamma\leq1 0 < γ ≤ 1 ; then the Q − 2 Q^{-2} Q − 2 error above is also O ( Q − γ ) O(Q^{-\gamma}) O ( Q − γ ) .
The density increment and completion of the proof
The following proposition gives the dichotomy that will be iterated in the proof of Theorem 1.1.
Proposition 8.1 (Density-increment). For the constant σ \sigma σ fixed in Section 4, there are constants 0 < κ < 1 0 < \kappa< 1 0 < κ < 1 and B 0 ≥ 1 B_0 \geq1 B 0 ≥ 1 , depending only on Δ \Delta Δ and d d d , with the following property. Let A ⊆ [ N ] d A \subseteq[N]^d A ⊆ [ N ] d be nonempty and contain no nondegenerate similar copy of Δ \Delta Δ , and set α : = ∣ A ∣ / N d \alpha:= |A|/N^d α := ∣ A ∣/ N d . Then one of the following alternatives holds:
N ≪ Δ , d α − B 0 N \ll_{\Delta,d} \alpha^{-B_0} N ≪ Δ , d α − B 0 .
There are an integer N ′ ≥ 1 N' \geq1 N ′ ≥ 1 and a set A ′ ⊆ [ N ′ ] d A' \subseteq[N']^d A ′ ⊆ [ N ′ ] d , also containing no nondegenerate similar copy of Δ \Delta Δ , such that N ′ ≫ Δ , d α B 0 N N' \gg_{\Delta,d} \alpha^{B_0}N N ′ ≫ Δ , d α B 0 N and ∣ A ′ ∣ / ( N ′ ) d > ( 1 + κ ) α |A'|/(N')^d > (1+\kappa)\alpha ∣ A ′ ∣/ ( N ′ ) d > ( 1 + κ ) α .
Proof. Let B 0 ≥ 1 B_0 \geq1 B 0 ≥ 1 , Q 0 ≥ 2 Q_0 \geq2 Q 0 ≥ 2 , and 0 < κ < 1 0 < \kappa< 1 0 < κ < 1 be constants to be chosen later depending only on Δ \Delta Δ and d d d . Put Q : = Q 0 α − B 0 / ( L ( L + 3 ) + 3 ) Q := Q_0\alpha^{-B_0/(L(L+3)+3)} Q := Q 0 α − B 0 / ( L ( L + 3 ) + 3 ) . If N < N 0 N < N_0 N < N 0 , then alternative (1) holds. Otherwise R ≥ σ N / 2 R \geq\sigma N/2 R ≥ σ N /2 , and if R < 4 Q L ( L + 3 ) + 3 R < 4Q^{L(L+3)+3} R < 4 Q L ( L + 3 ) + 3 , then
N ≪ Δ , d Q L ( L + 3 ) + 3 = Q 0 L ( L + 3 ) + 3 α − B 0 ≪ Δ , d α − B 0 , N \ll_{\Delta,d} Q^{L(L+3)+3}=Q_0^{L(L+3)+3}\alpha^{-B_0}\ll_{\Delta,d}\alpha^{-B_0}, N ≪ Δ , d Q L ( L + 3 ) + 3 = Q 0 L ( L + 3 ) + 3 α − B 0 ≪ Δ , d α − B 0 ,
so alternative (1) again holds. We may therefore assume (56) . In particular, 2 ≤ Q ≤ R 2 \leq Q \leq R 2 ≤ Q ≤ R , and the major- and minor arc estimates apply.
Let B \mathcal{B} B and s = ∣ B ∣ s=|\mathcal{B}| s = ∣ B ∣ be supplied by (28) , and take F i = g F_i=g F i = g for i ∈ B i\in\mathcal{B} i ∈ B and F i = 1 [ N ] d F_i=\mathbf{1}_{[N]^d} F i = 1 [ N ] d otherwise. Then
α s N d R ( K − 1 ) d − 2 L ≪ Δ , d ∣ Λ N M Q ( F 0 , … , F K − 1 ) ∣ + ∣ Λ N m Q ( F 0 , … , F K − 1 ) ∣ . \alpha^sN^dR^{(K-1)d-2L}\ll_{\Delta,d}\left|\Lambda_N^{\mathfrak{M}_Q}(F_0,\ldots,F_{K-1})\right|+\left|\Lambda_N^{\mathfrak{m}_Q}(F_0,\ldots,F_{K-1})\right|. α s N d R ( K − 1 ) d − 2 L ≪ Δ , d Λ N M Q ( F 0 , … , F K − 1 ) + Λ N m Q ( F 0 , … , F K − 1 ) .
The bounds in (26) give
∏ i = 0 K − 1 ∥ F i ∥ ℓ 2 ( Z d ) = ∥ g ∥ ℓ 2 ( Z d ) s ∥ 1 [ N ] d ∥ ℓ 2 ( Z d ) K − s ≤ α s / 2 N K d / 2 . \prod_{i=0}^{K-1}\lVert F_i\rVert_{\ell^2(\mathbb{Z}^d)}
=\lVert g\rVert_{\ell^2(\mathbb{Z}^d)}^s\lVert\mathbf{1}_{[N]^d}\rVert_{\ell^2(\mathbb{Z}^d)}^{K-s}
\leq\alpha^{s/2}N^{Kd/2}. i = 0 ∏ K − 1 ∥ F i ∥ ℓ 2 ( Z d ) = ∥ g ∥ ℓ 2 ( Z d ) s ∥ 1 [ N ] d ∥ ℓ 2 ( Z d ) K − s ≤ α s /2 N K d /2 .
Since μ q \mu_q μ q is a probability measure, Young’s inequality gives
∥ μ q ∗ 1 [ N ] d ∥ ℓ K ( Z d ) ≤ ∥ μ q ∥ ℓ 1 ( Z d ) ∥ 1 [ N ] d ∥ ℓ K ( Z d ) = N d / K . \lVert\mu_q * \mathbf{1}_{[N]^d}\rVert_{\ell^K(\mathbb{Z}^d)}
\leq\lVert\mu_q\rVert_{\ell^1(\mathbb{Z}^d)}\lVert\mathbf{1}_{[N]^d}\rVert_{\ell^K(\mathbb{Z}^d)}
=N^{d/K}. ∥ μ q ∗ 1 [ N ] d ∥ ℓ K ( Z d ) ≤ ∥ μ q ∥ ℓ 1 ( Z d ) ∥ 1 [ N ] d ∥ ℓ K ( Z d ) = N d / K .
Consequently,
∏ i = 0 K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) = ∥ μ q ∗ g ∥ ℓ K ( Z d ) ∥ μ q ∗ 1 [ N ] d ∥ ℓ K ( Z d ) K − s ≤ N d ( K − s ) / K ( ∑ x ∈ Z d ∣ μ q ∗ g ( x ) ∣ K ) s / K = N d ( 1 N d ∑ x ∈ Z d ∣ μ q ∗ g ( x ) ∣ K ) s / K , \begin{aligned}
\prod_{i=0}^{K-1}\|\mu_q * F_i\|_{\ell^K(\mathbb{Z}^d)}=\|\mu_q * g\|_{\ell^K(\mathbb{Z}^d)}\|\mu_q * \mathbf{1}_{[N]^d}\|_{\ell^K(\mathbb{Z}^d)}^{K-s} \\
&\leq N^{d(K-s)/K}\left(\sum_{x\in\mathbb{Z}^d}|\mu_q*g(x)|^K\right)^{s/K} \\
&=N^d\left(\frac{1}{N^d}\sum_{x\in\mathbb{Z}^d}|\mu_q*g(x)|^K\right)^{s/K},
\end{aligned} i = 0 ∏ K − 1 ∥ μ q ∗ F i ∥ ℓ K ( Z d ) = ∥ μ q ∗ g ∥ ℓ K ( Z d ) ∥ μ q ∗ 1 [ N ] d ∥ ℓ K ( Z d ) K − s ≤ N d ( K − s ) / K ( x ∈ Z d ∑ ∣ μ q ∗ g ( x ) ∣ K ) s / K = N d ( N d 1 x ∈ Z d ∑ ∣ μ q ∗ g ( x ) ∣ K ) s / K ,
where d ( K − s ) / K + d s / K = d d(K-s)/K+ds/K=d d ( K − s ) / K + d s / K = d .
Since R ≍ Δ N R\asymp_{\Delta}N R ≍ Δ N and Q − 2 ≤ Q − γ Q^{-2}\leq Q^{-\gamma} Q − 2 ≤ Q − γ , Propositions (44) and (56) imply
α s ≪ Δ , d ∑ q ≤ 2 Q L q − 1 − η ( 1 N d ∑ x ∈ Z d ∣ μ q ∗ g ( x ) ∣ K ) s / K + Q − γ α s / 2 . (84) \alpha^s\ll_{\Delta,d}\sum_{q\leq2Q^L}q^{-1-\eta}\left(\frac{1}{N^d}\sum_{x\in\mathbb{Z}^d}|\mu_q*g(x)|^K\right)^{s/K}+Q^{-\gamma}\alpha^{s/2}. \tag*{(84)} α s ≪ Δ , d q ≤ 2 Q L ∑ q − 1 − η ( N d 1 x ∈ Z d ∑ ∣ μ q ∗ g ( x ) ∣ K ) s / K + Q − γ α s /2 . (84)
Assuming B 0 B_0 B 0 is sufficiently large depending only on K K K and d d d , the exponent of α \alpha α below is at least s s s , and hence
Q − γ α s / 2 = Q 0 − γ α γ B 0 / ( L ( L + 3 ) + 3 ) + s / 2 ≤ Q 0 − γ α s . Q^{-\gamma}\alpha^{s/2}=Q_0^{-\gamma}\alpha^{\gamma B_0/(L(L+3)+3)+s/2}\leq Q_0^{-\gamma}\alpha^s. Q − γ α s /2 = Q 0 − γ α γ B 0 / ( L ( L + 3 ) + 3 ) + s /2 ≤ Q 0 − γ α s .
The contribution of this term to (84) is therefore O Δ , d ( Q 0 − γ α s ) O_{\Delta,d}(Q_0^{-\gamma}\alpha^s) O Δ , d ( Q 0 − γ α s ) . Assuming Q 0 Q_0 Q 0 is sufficiently large depending only on Δ \Delta Δ and d d d , this contribution is at most α s / 2 \alpha^s/2 α s /2 and may be moved to the left-hand side. Since ∑ q ≥ 1 q − 1 − η < ∞ \sum_{q\geq1}q^{-1-\eta}<\infty ∑ q ≥ 1 q − 1 − η < ∞ , there is therefore some q ≤ 2 Q L q\leq2Q^L q ≤ 2 Q L such that
∑ x ∈ Z d ∣ μ q ∗ g ( x ) ∣ K ≫ Δ , d α K N d . (85) \sum_{x\in\mathbb{Z}^d}|\mu_q*g(x)|^K\gg_{\Delta,d}\alpha^KN^d. \tag*{(85)} x ∈ Z d ∑ ∣ μ q ∗ g ( x ) ∣ K ≫ Δ , d α K N d . (85)
Recall that N q = ⌊ R / ( q Q L ( L + 2 ) + 3 ) ⌋ N_q=\left\lfloor R/(qQ^{L(L+2)+3})\right\rfloor N q = ⌊ R / ( q Q L ( L + 2 ) + 3 ) ⌋ . The function μ q ∗ g \mu_q*g μ q ∗ g is supported on [ 1 , N + q ( N q − 1 ) ] d [1,N+q(N_q-1)]^d [ 1 , N + q ( N q − 1 ) ] d , and
∣ [ 1 , N + q ( N q − 1 ) ] d ∖ [ 1 + q ( N q − 1 ) , N ] d ∣ ≪ d q N q N d − 1 |[1,N+q(N_q-1)]^d\setminus[1+q(N_q-1),N]^d|\ll_d qN_qN^{d-1} ∣ [ 1 , N + q ( N q − 1 ) ] d ∖ [ 1 + q ( N q − 1 ) , N ] d ∣ ≪ d q N q N d − 1
≪ Δ , d Q − L ( L + 2 ) − 3 N d \ll_{\Delta,d}Q^{-L(L+2)-3}N^d ≪ Δ , d Q − L ( L + 2 ) − 3 N d
≪ Δ , d Q 0 − L ( L + 2 ) − 3 α B 0 ( L ( L + 2 ) + 3 ) / ( L ( L + 3 ) + 3 ) N d . \ll_{\Delta,d}Q_0^{-L(L+2)-3} \alpha^{B_0(L(L+2)+3)/(L(L+3)+3)}N^d. ≪ Δ , d Q 0 − L ( L + 2 ) − 3 α B 0 ( L ( L + 2 ) + 3 ) / ( L ( L + 3 ) + 3 ) N d .
Assuming B 0 B_0 B 0 is sufficiently large depending only on K K K , the exponent of α \alpha α in the last line is at least K K K , so
∣ [ 1 , N + q ( N q − 1 ) ] d ∖ [ 1 + q ( N q − 1 ) , N ] d ∣ ≪ Δ , d Q 0 − L ( L + 2 ) − 3 α K N d . (86) |[1,N+q(N_q-1)]^d\setminus[1+q(N_q-1),N]^d|\ll_{\Delta,d}Q_0^{-L(L+2)-3}\alpha^KN^d. \tag*{(86)} ∣ [ 1 , N + q ( N q − 1 ) ] d ∖ [ 1 + q ( N q − 1 ) , N ] d ∣ ≪ Δ , d Q 0 − L ( L + 2 ) − 3 α K N d . (86)
Since μ q \mu_q μ q is a probability measure and ∑ x ∈ Z d g ( x ) = 0 \sum_{x\in\mathbb{Z}^d}g(x)=0 ∑ x ∈ Z d g ( x ) = 0 , we have ∑ x ∈ Z d ( μ q ∗ g ) ( x ) = 0 \sum_{x\in\mathbb{Z}^d}(\mu_q*g)(x)=0 ∑ x ∈ Z d ( μ q ∗ g ) ( x ) = 0 . Consequently,
∑ x ∈ Z d ( μ q ∗ g ) + ( x ) = ∑ x ∈ Z d ( μ q ∗ g ) − ( x ) = 1 2 ∑ x ∈ Z d ∣ ( μ q ∗ g ) ( x ) ∣ . (87) \sum_{x\in\mathbb{Z}^d}(\mu_q*g)_+(x)=\sum_{x\in\mathbb{Z}^d}(\mu_q*g)_-(x)=\frac{1}{2}\sum_{x\in\mathbb{Z}^d}|(\mu_q*g)(x)|. \tag*{(87)} x ∈ Z d ∑ ( μ q ∗ g ) + ( x ) = x ∈ Z d ∑ ( μ q ∗ g ) − ( x ) = 2 1 x ∈ Z d ∑ ∣ ( μ q ∗ g ) ( x ) ∣. (87)
Since g ≥ − α g\geq-\alpha g ≥ − α and ∣ g ∣ ≤ 1 |g|\leq1 ∣ g ∣ ≤ 1 , we also have
0 ≤ ( μ q ∗ g ) − ≤ α , ∣ μ q ∗ g ∣ ≤ 1. 0\leq(\mu_q*g)_-\leq\alpha,\qquad|\mu_q*g|\leq1. 0 ≤ ( μ q ∗ g ) − ≤ α , ∣ μ q ∗ g ∣ ≤ 1.
Assuming Q 0 Q_0 Q 0 is sufficiently large depending only on Δ \Delta Δ and d d d , (86) and (85) give
∑ x ∈ [ 1 + q ( N q − 1 ) , N ] d ∣ ( μ q ∗ g ) ( x ) ∣ K ≫ Δ , d α K N d . (88) \sum_{x\in[1+q(N_q-1),N]^d}|(\mu_q*g)(x)|^K\gg_{\Delta,d}\alpha^KN^d. \tag*{(88)} x ∈ [ 1 + q ( N q − 1 ) , N ] d ∑ ∣ ( μ q ∗ g ) ( x ) ∣ K ≫ Δ , d α K N d . (88)
On [ 1 + q ( N q − 1 ) , N ] d [1+q(N_q-1),N]^d [ 1 + q ( N q − 1 ) , N ] d we have μ q ∗ g = μ q ∗ 1 A − α \mu_q * g=\mu_q * \mathbf{1}_A-\alpha μ q ∗ g = μ q ∗ 1 A − α . Since
∣ ( μ q ∗ g ) ( x ) ∣ K = ( ( μ q ∗ g ) + ( x ) ) K + ( ( μ q ∗ g ) − ( x ) ) K , \lvert(\mu_q*g)(x)\rvert^K=((\mu_q*g)_+(x))^K+((\mu_q*g)_-(x))^K, ∣( μ q ∗ g ) ( x ) ∣ K = (( μ q ∗ g ) + ( x ) ) K + (( μ q ∗ g ) − ( x ) ) K ,
(8.5) implies that at least one of the following two estimates holds:
∑ x ∈ [ 1 + q ( N q − 1 ) , N ] d ( ( μ q ∗ g ) + ( x ) ) K ≫ Δ , d α K N d . (89) \sum_{x\in[1+q(N_q-1),N]^d}((\mu_q*g)_+(x))^K\gg_{\Delta,d}\alpha^K N^d. \tag*{(89)} x ∈ [ 1 + q ( N q − 1 ) , N ] d ∑ (( μ q ∗ g ) + ( x ) ) K ≫ Δ , d α K N d . (89)
∑ x ∈ [ 1 + q ( N q − 1 ) , N ] d ( ( μ q ∗ g ) − ( x ) ) K ≫ Δ , d α K N d . (90) \sum_{x\in[1+q(N_q-1),N]^d}((\mu_q*g)_-(x))^K\gg_{\Delta,d}\alpha^K N^d. \tag*{(90)} x ∈ [ 1 + q ( N q − 1 ) , N ] d ∑ (( μ q ∗ g ) − ( x ) ) K ≫ Δ , d α K N d . (90)
If (89) holds, pigeonholing over at most N d N^d N d points gives some y ∈ [ 1 + q ( N q − 1 ) , N ] d y\in[1+q(N_q-1),N]^d y ∈ [ 1 + q ( N q − 1 ) , N ] d such that ( μ q ∗ g ) ( y ) ≫ Δ , d α (\mu_q*g)(y)\gg_{\Delta,d}\alpha ( μ q ∗ g ) ( y ) ≫ Δ , d α . Suppose instead that (90) holds. Since ( μ q ∗ g ) − ≤ α (\mu_q*g)_-\leq\alpha ( μ q ∗ g ) − ≤ α ,
∑ x ∈ [ 1 + q ( N q − 1 ) , N ] d ( μ q ∗ g ) − ( x ) ≥ α 1 − K ∑ x ∈ [ 1 + q ( N q − 1 ) , N ] d ( ( μ q ∗ g ) − ( x ) ) K ≫ Δ , d α N d . \sum_{x\in[1+q(N_q-1),N]^d}(\mu_q*g)_-(x)\geq\alpha^{1-K}\sum_{x\in[1+q(N_q-1),N]^d}((\mu_q*g)_-(x))^K\gg_{\Delta,d}\alpha N^d. x ∈ [ 1 + q ( N q − 1 ) , N ] d ∑ ( μ q ∗ g ) − ( x ) ≥ α 1 − K x ∈ [ 1 + q ( N q − 1 ) , N ] d ∑ (( μ q ∗ g ) − ( x ) ) K ≫ Δ , d α N d .
It follows from (8.4) that
∑ x ∈ Z d ( μ q ∗ g ) + ( x ) = ∑ x ∈ Z d ( μ q ∗ g ) − ( x ) ≫ Δ , d α N d . (91) \sum_{x\in\mathbb{Z}^d}(\mu_q*g)_+(x)=\sum_{x\in\mathbb{Z}^d}(\mu_q*g)_-(x)\gg_{\Delta,d}\alpha N^d. \tag*{(91)} x ∈ Z d ∑ ( μ q ∗ g ) + ( x ) = x ∈ Z d ∑ ( μ q ∗ g ) − ( x ) ≫ Δ , d α N d . (91)
Since μ q ∗ g \mu_q*g μ q ∗ g is supported on [ 1 , N + q ( N q − 1 ) ] d [1,N+q(N_q-1)]^d [ 1 , N + q ( N q − 1 ) ] d and ( μ q ∗ g ) + ≤ 1 (\mu_q*g)_+\leq1 ( μ q ∗ g ) + ≤ 1 , (8.3) gives
∑ x ∈ [ 1 , N + q ( N q − 1 ) ] d ∖ [ 1 + q ( N q − 1 ) , N ] d ( μ q ∗ g ) + ( x ) = O Δ , d ( Q 0 − L ( L + 2 ) − 3 α K N d ) . \sum_{x\in[1,N+q(N_q-1)]^d\setminus[1+q(N_q-1),N]^d}(\mu_q*g)_+(x)=O_{\Delta,d}\left(Q_0^{-L(L+2)-3}\alpha^K N^d\right). x ∈ [ 1 , N + q ( N q − 1 ) ] d ∖ [ 1 + q ( N q − 1 ) , N ] d ∑ ( μ q ∗ g ) + ( x ) = O Δ , d ( Q 0 − L ( L + 2 ) − 3 α K N d ) .
Assuming Q 0 Q_0 Q 0 is sufficiently large depending only on Δ \Delta Δ and d d d , this estimate, (8.8), and α K ≤ α \alpha^K\leq\alpha α K ≤ α give
∑ x ∈ [ 1 + q ( N q − 1 ) , N ] d ( μ q ∗ g ) + ( x ) ≫ Δ , d α N d . \sum_{x\in[1+q(N_q-1),N]^d}(\mu_q*g)_+(x)\gg_{\Delta,d}\alpha N^d. x ∈ [ 1 + q ( N q − 1 ) , N ] d ∑ ( μ q ∗ g ) + ( x ) ≫ Δ , d α N d .
Pigeonholing over at most N d N^d N d points again gives some y ∈ [ 1 + q ( N q − 1 ) , N ] d y\in[1+q(N_q-1),N]^d y ∈ [ 1 + q ( N q − 1 ) , N ] d such that ( μ q ∗ g ) ( y ) ≫ Δ , d α (\mu_q*g)(y)\gg_{\Delta,d}\alpha ( μ q ∗ g ) ( y ) ≫ Δ , d α . Thus, assuming κ \kappa κ is sufficiently small depending only on Δ \Delta Δ and d d d , there is some y ∈ [ 1 + q ( N q − 1 ) , N ] d y\in[1+q(N_q-1),N]^d y ∈ [ 1 + q ( N q − 1 ) , N ] d such that
( μ q ∗ 1 A ) ( y ) = ( μ q ∗ g ) ( y ) + α > ( 1 + κ ) α . (\mu_q*\mathbf{1}_A)(y)=(\mu_q*g)(y)+\alpha>(1+\kappa)\alpha. ( μ q ∗ 1 A ) ( y ) = ( μ q ∗ g ) ( y ) + α > ( 1 + κ ) α .
Set N ′ : = N q N':=N_q N ′ := N q . Since q ≤ 2 Q L q\leq2Q^L q ≤ 2 Q L and (7.1) holds,
R q Q L ( L + 2 ) + 3 ≥ R 2 Q L ( L + 3 ) + 3 ≥ 2. \frac{R}{qQ^{L(L+2)+3}}\geq\frac{R}{2Q^{L(L+3)+3}}\geq2. q Q L ( L + 2 ) + 3 R ≥ 2 Q L ( L + 3 ) + 3 R ≥ 2.
It follows that
N ′ = ⌊ R q Q L ( L + 2 ) + 3 ⌋ ≥ R 2 q Q L ( L + 2 ) + 3 ≥ R 4 Q L ( L + 3 ) + 3 ≫ Δ , d α B 0 N . N'=\left\lfloor\frac{R}{qQ^{L(L+2)+3}}\right\rfloor\geq\frac{R}{2qQ^{L(L+2)+3}}\geq\frac{R}{4Q^{L(L+3)+3}}\gg_{\Delta,d}\alpha^{B_0}N. N ′ = ⌊ q Q L ( L + 2 ) + 3 R ⌋ ≥ 2 q Q L ( L + 2 ) + 3 R ≥ 4 Q L ( L + 3 ) + 3 R ≫ Δ , d α B 0 N .
Finally, put
A ′ : = { n ∈ [ N ′ ] d : y − q ( n − ( 1 , … , 1 ) ) ∈ A } . A':=\left\{n\in[N']^d:y-q(n-(1,\ldots,1))\in A\right\}. A ′ := { n ∈ [ N ′ ] d : y − q ( n − ( 1 , … , 1 )) ∈ A } .
Because y ∈ [ 1 + q ( N q − 1 ) , N ] d y\in[1+q(N_q-1),N]^d y ∈ [ 1 + q ( N q − 1 ) , N ] d and N ′ = N q N'=N_q N ′ = N q , the map n ↦ y − q ( n − ( 1 , … , 1 ) ) n\mapsto y-q(n-(1,\ldots,1)) n ↦ y − q ( n − ( 1 , … , 1 )) sends [ N ′ ] d [N']^d [ N ′ ] d injectively into [ N ] d [N]^d [ N ] d . By the definition of μ q \mu_q μ q ,
∣ A ′ ∣ ( N ′ ) d = ( μ q ∗ 1 A ) ( y ) > ( 1 + κ ) α . \frac{|A'|}{(N')^d}=(\mu_q*\mathbf{1}_A)(y)>(1+\kappa)\alpha. ( N ′ ) d ∣ A ′ ∣ = ( μ q ∗ 1 A ) ( y ) > ( 1 + κ ) α .
The map n ↦ y − q ( n − ( 1 , … , 1 ) ) n\mapsto y-q(n-(1,\ldots,1)) n ↦ y − q ( n − ( 1 , … , 1 )) is a composition of translations, a reflection, and a dilation, so A ′ A' A ′ remains Δ \Delta Δ -free. This proves alternative (2).
Proof of Theorem 1.1. Fix the constants supplied by Proposition 8.1. If A = ∅ A = \varnothing A = ∅ , there is nothing to prove. Otherwise, set A 0 = A A_0 = A A 0 = A , N 0 = N N_0 = N N 0 = N , and, at every stage j j j , put α j : = ∣ A j ∣ / N j d \alpha_j := |A_j|/N_j^d α j := ∣ A j ∣/ N j d . Apply Proposition 8.1 at each stage. If alternative (1) holds, stop. If alternative (2) holds, let A j + 1 ⊆ [ N j + 1 ] d A_{j+1} \subseteq[N_{j+1}]^d A j + 1 ⊆ [ N j + 1 ] d be the set it supplies. Alternative (2) cannot hold indefinitely, since each iteration increases the density by a factor greater than 1 + κ 1+\kappa 1 + κ , whereas every density is at most 1 1 1 . Let j ∗ j_* j ∗ be the first stage at which alternative (1) holds. Then
N j ∗ ≪ Δ , d α j ∗ − B 0 . (92) N_{j_*} \ll_{\Delta,d} \alpha_{j_*}^{-B_0}. \tag*{(92)} N j ∗ ≪ Δ , d α j ∗ − B 0 . (92)
Iterating the density increase in alternative (2) gives 1 ≥ α j ∗ ≥ ( 1 + κ ) j ∗ α 0 1 \geq\alpha_{j_*} \geq(1+\kappa)^{j_*}\alpha_0 1 ≥ α j ∗ ≥ ( 1 + κ ) j ∗ α 0 , and hence j ∗ ≪ Δ , d 1 + log ( 1 / α 0 ) j_* \ll_{\Delta,d} 1+\log(1/\alpha_0) j ∗ ≪ Δ , d 1 + log ( 1/ α 0 ) . The bound N j + 1 ≫ Δ , d α j B 0 N j N_{j+1} \gg_{\Delta,d} \alpha_j^{B_0}N_j N j + 1 ≫ Δ , d α j B 0 N j in alternative (2) gives, for 0 ≤ j < j ∗ 0 \leq j < j_* 0 ≤ j < j ∗ ,
log N j ≤ log N j + 1 + B 0 log ( 1 / α j ) + O Δ , d ( 1 ) . \log N_j \leq\log N_{j+1} + B_0\log(1/\alpha_j) + O_{\Delta,d}(1). log N j ≤ log N j + 1 + B 0 log ( 1/ α j ) + O Δ , d ( 1 ) .
Summing over j j j and applying (92) , we obtain
log N 0 ≤ B 0 ∑ j = 0 j ∗ log ( 1 / α j ) + O Δ , d ( j ∗ + 1 ) . \log N_0 \leq B_0\sum_{j=0}^{j_*}\log(1/\alpha_j) + O_{\Delta,d}(j_*+1). log N 0 ≤ B 0 j = 0 ∑ j ∗ log ( 1/ α j ) + O Δ , d ( j ∗ + 1 ) .
Since α j ≥ α 0 \alpha_j \geq\alpha_0 α j ≥ α 0 for every j j j , it follows that
log N 0 ≪ Δ , d ( 1 + log ( 1 / α 0 ) ) 2 . \log N_0 \ll_{\Delta,d} \left(1+\log(1/\alpha_0)\right)^2. log N 0 ≪ Δ , d ( 1 + log ( 1/ α 0 ) ) 2 .
Consequently, for suitable constants C , c > 0 C,c>0 C , c > 0 depending only on Δ \Delta Δ and d d d ,
α 0 ≤ C exp ( − c log N 0 ) . \alpha_0 \leq C\exp\left(-c\sqrt{\log N_0}\right). α 0 ≤ C exp ( − c log N 0 ) .
Since α 0 = ∣ A ∣ / N 0 d \alpha_0=|A|/N_0^d α 0 = ∣ A ∣/ N 0 d , this proves the theorem. □
Application to a continuous variant of the problem
Proof of Corollary 1.3. Let C 0 , c 0 > 0 C_0,c_0>0 C 0 , c 0 > 0 be the constants in Theorem 1.1, and put L α : = 1 + log ( 1 / α ) L_\alpha:= 1+\log(1/\alpha) L α := 1 + log ( 1/ α ) . Choose D = D Δ , d D=D_{\Delta,d} D = D Δ , d sufficiently large and set
N : = ⌈ exp ( D L α 2 ) ⌉ . N := \left\lceil\exp(DL_\alpha^2)\right\rceil. N := ⌈ exp ( D L α 2 ) ⌉ .
Then C 0 exp ( − c 0 log N ) < α C_0\exp(-c_0\sqrt{\log N})<\alpha C 0 exp ( − c 0 log N ) < α and N ≤ 2 exp ( D L α 2 ) N\leq2\exp(DL_\alpha^2) N ≤ 2 exp ( D L α 2 ) . For u ∈ [ 0 , 1 / N ) d u\in[0,1/N)^d u ∈ [ 0 , 1/ N ) d , define
B u : = { m ∈ { 0 , … , N − 1 } d : u + m N ∈ E } . B_u := \left\{m\in\{0,\ldots,N-1\}^d : u+\frac{m}{N}\in E\right\}. B u := { m ∈ { 0 , … , N − 1 } d : u + N m ∈ E } .
The translates of [ 0 , 1 / N ) d [0,1/N)^d [ 0 , 1/ N ) d by the vectors m / N m/N m / N partition [ 0 , 1 ) d [0,1)^d [ 0 , 1 ) d , and therefore Fubini’s theorem gives
∫ [ 0 , 1 / N ) d ∣ B u ∣ d u = ∣ E ∣ . \int_{[0,1/N)^d}|B_u|\,du=|E|. ∫ [ 0 , 1/ N ) d ∣ B u ∣ d u = ∣ E ∣.
There is consequently some u u u such that ∣ B u ∣ ≥ α N d |B_u|\geq\alpha N^d ∣ B u ∣ ≥ α N d . By the choice of N N N and Theorem 1.1, the translate B u + ( 1 , … , 1 ) ⊆ [ N ] d B_u+(1,\ldots,1)\subseteq[N]^d B u + ( 1 , … , 1 ) ⊆ [ N ] d contains a nondegenerate similar copy of Δ \Delta Δ . Translating back, let y 0 , … , y K − 1 ∈ B u y_0,\ldots,y_{K-1}\in B_u y 0 , … , y K − 1 ∈ B u be its vertices, and let ρ > 0 \rho>0 ρ > 0 be its similarity ratio. For any fixed edge { i , j } \{i,j\} { i , j } of Δ \Delta Δ , the vector y i − y j y_i-y_j y i − y j is a nonzero integer vector. Hence
ρ 2 = ∣ y i − y j ∣ 2 ∣ v i − v j ∣ 2 ≥ 1 ∣ v i − v j ∣ 2 , \rho^2=\frac{\lvert y_i-y_j\rvert^2}{\lvert v_i-v_j\rvert^2}\geq\frac{1}{\lvert v_i-v_j\rvert^2}, ρ 2 = ∣ v i − v j ∣ 2 ∣ y i − y j ∣ 2 ≥ ∣ v i − v j ∣ 2 1 ,
so ρ ≥ c Δ > 0 \rho\geq c_\Delta>0 ρ ≥ c Δ > 0 . Mapping the copy into E E E by m ↦ u + m / N m\mapsto u+m/N m ↦ u + m / N gives a similar copy with ratio
λ = ρ N ≥ exp ( − C Δ , d ( 1 + log ( 1 / α ) ) 2 ) , \lambda=\frac{\rho}{N}\geq\exp\left(-C_{\Delta,d}\left(1+\log(1/\alpha)\right)^2\right), λ = N ρ ≥ exp ( − C Δ , d ( 1 + log ( 1/ α ) ) 2 ) ,
after enlarging C Δ , d C_{\Delta,d} C Δ , d . Since the pairwise distances determine a nondegenerate simplex up to Euclidean isometry, this copy has the form x + λ U Δ x+\lambda U\Delta x + λ U Δ for some x ∈ R d x\in\mathbb{R}^d x ∈ R d and U ∈ O ( d ) U\in O(d) U ∈ O ( d ) .
Counting solutions to the matrix equation
The trace expansion in Lemma 2.1 2.1 2.1 reduces the required high-moment estimate to counting bounded integer matrices U U U and V V V satisfying U V T = 0 UV^{\mathsf{T}}=0 U V T = 0 . The precise estimate needed in the main argument is the following.
Lemma B.1 (Integer matrix orthogonality). Let s , t , n s,t,n s , t , n be positive integers with
n ≥ 2 max ( s , t ) and n > s + t . n\geq2\max(s,t)\qquad\text{and}\qquad n>s+t. n ≥ 2 max ( s , t ) and n > s + t .
Then, for every positive integer R R R ,
# { ( U , V ) : U ∈ ( [ − R , R ] ∩ Z ) s × n , V ∈ ( [ − R , R ] ∩ Z ) t × n , U V T = 0 } ≪ n , s , t R n ( s + t ) − 2 s t . \#\left\{(U,V):
\begin{aligned}
&U\in([-R,R]\cap\mathbb{Z})^{s\times n},\\
&V\in([-R,R]\cap\mathbb{Z})^{t\times n},\\
&UV^{\mathsf{T}}=0
\end{aligned}
\right\}\ll_{n,s,t}R^{n(s+t)-2st}. # ⎩ ⎨ ⎧ ( U , V ) : U ∈ ([ − R , R ] ∩ Z ) s × n , V ∈ ([ − R , R ] ∩ Z ) t × n , U V T = 0 ⎭ ⎬ ⎫ ≪ n , s , t R n ( s + t ) − 2 s t .
In the application, n = K + 1 n=K+1 n = K + 1 , s = ∣ S ∣ s=\lvert S\rvert s = ∣ S ∣ , and t = ∣ T ∣ t=\lvert T\rvert t = ∣ T ∣ . Thus n > s + t n>s+t n > s + t follows from ∣ S ∣ + ∣ T ∣ = K \lvert S\rvert+\lvert T\rvert=K ∣ S ∣ + ∣ T ∣ = K , while n ≥ 2 max ( s , t ) n\geq2\max(s,t) n ≥ 2 max ( s , t ) follows from the balancedness of the cut S ∣ T S\mid T S ∣ T . We now develop the lattice terminology needed for the proof.
Lattice preliminaries
The definitions and elementary determinant facts below are standard; see [2 ] , Chapter I. We follow [12 ] , §1 for the determinant convention and for the notation X n X_n X n , μ n \mu_n μ n , Gr ( L , d ) \operatorname{Gr}(L,d) Gr ( L , d ) , and rational flags.
A lattice in R n \mathbb{R}^n R n is a subgroup of the form Γ = Z γ 1 + ⋯ + Z γ d \Gamma=\mathbb{Z}\gamma_1+\cdots+\mathbb{Z}\gamma_d Γ = Z γ 1 + ⋯ + Z γ d , where γ 1 , … , γ d ∈ R n \gamma_1,\ldots,\gamma_d\in\mathbb{R}^n γ 1 , … , γ d ∈ R n are linearly independent. These vectors form a basis of Γ \Gamma Γ , and d d d is its rank. The real span of Γ \Gamma Γ is
span R Γ : = R γ 1 + ⋯ + R γ d . \operatorname{span}_{\mathbb{R}}\Gamma:=\mathbb{R}\gamma_1+\cdots+\mathbb{R}\gamma_d. span R Γ := R γ 1 + ⋯ + R γ d .
A sublattice of a lattice L L L is a subgroup Γ ⊆ L \Gamma\subseteq L Γ ⊆ L ; every such subgroup is itself a lattice. The Gram matrix of the basis γ 1 , … , γ d \gamma_1,\ldots,\gamma_d γ 1 , … , γ d is
G ( γ 1 , … , γ d ) : = ( ⟨ γ i , γ j ⟩ ) i , j = 1 d . G(\gamma_1,\ldots,\gamma_d):=\left(\langle\gamma_i,\gamma_j\rangle\right)_{i,j=1}^d. G ( γ 1 , … , γ d ) := ( ⟨ γ i , γ j ⟩ ) i , j = 1 d .
Following Kim, define the determinant of Γ \Gamma Γ by
det Γ : = ∥ γ 1 ∧ ⋯ ∧ γ d ∥ = ( det G ( γ 1 , … , γ d ) ) 1 / 2 . \det\Gamma:=\lVert\gamma_1\wedge\cdots\wedge\gamma_d\rVert=\left(\det G(\gamma_1,\ldots,\gamma_d)\right)^{1/2}. det Γ := ∥ γ 1 ∧ ⋯ ∧ γ d ∥ = ( det G ( γ 1 , … , γ d ) ) 1/2 .
Any two bases of Γ \Gamma Γ differ by a matrix A ∈ GL d ( Z ) A \in\operatorname{GL}_{d}(\mathbb{Z}) A ∈ GL d ( Z ) . Under this change of basis, the Gram matrix G G G is replaced by A G A T AGA^{\mathsf{T}} A G A T , whose determinant equals det G \det G det G . Thus det Γ \det\Gamma det Γ does not depend on the chosen basis.
If Γ ⊆ Λ \Gamma\subseteq\Lambda Γ ⊆ Λ have the same rank, then the index [ Λ : Γ ] [\Lambda: \Gamma] [ Λ : Γ ] , namely the number of cosets of Γ \Gamma Γ in Λ \Lambda Λ , is finite, and
det Γ = [ Λ : Γ ] det Λ . (93) \det\Gamma= [\Lambda: \Gamma]\det\Lambda. \tag*{(93)} det Γ = [ Λ : Γ ] det Λ. (93)
Thus enlarging a lattice without changing its rank can only decrease its determinant. Moreover, if Γ ⊆ Z n \Gamma\subseteq\mathbb{Z}^{n} Γ ⊆ Z n , then the Gram matrix of any basis of Γ \Gamma Γ has integer entries and positive integer determinant, so
det Γ ≥ 1. (94) \det\Gamma\ge1. \tag*{(94)} det Γ ≥ 1. (94)
A lattice in R n \mathbb{R}^{n} R n is full-rank if it has rank n n n , and it is unimodular if it is full-rank and has determinant one. If g ∈ GL n ( R ) g \in\operatorname{GL}_{n}(\mathbb{R}) g ∈ GL n ( R ) , then Z n g : = { z g : z ∈ Z n } \mathbb{Z}^{n}g := \{zg : z \in\mathbb{Z}^{n}\} Z n g := { z g : z ∈ Z n } is a full-rank lattice of determinant ∣ det g ∣ |\det g| ∣ det g ∣ , where vectors are regarded as row vectors. In particular, Z n g \mathbb{Z}^{n}g Z n g is unimodular when g ∈ SL ( n , R ) g \in\operatorname{SL}(n,\mathbb{R}) g ∈ SL ( n , R ) .
Following Kim, let X n : = SL ( n , Z ) \ SL ( n , R ) X_n := \operatorname{SL}(n,\mathbb{Z}) \backslash\operatorname{SL}(n,\mathbb{R}) X n := SL ( n , Z ) \ SL ( n , R ) . The coset of g ∈ SL ( n , R ) g \in\operatorname{SL}(n,\mathbb{R}) g ∈ SL ( n , R ) corresponds to the unimodular row lattice Z n g \mathbb{Z}^{n}g Z n g . Left multiplication by an element of SL ( n , Z ) \operatorname{SL}(n,\mathbb{Z}) SL ( n , Z ) merely changes the chosen lattice basis, and every unimodular lattice has such a basis matrix. Thus X n X_n X n is the space of unimodular lattices in R n \mathbb{R}^{n} R n . We equip it with the invariant probability measure μ n \mu_n μ n induced by Haar measure on SL ( n , R ) \operatorname{SL}(n,\mathbb{R}) SL ( n , R ) .
A sublattice Γ ⊆ L \Gamma\subseteq L Γ ⊆ L is primitive in L L L if Γ = L ∩ span R Γ \Gamma= L \cap\operatorname{span}_{\mathbb{R}}\Gamma Γ = L ∩ span R Γ . For an arbitrary sublattice Γ ⊆ L \Gamma\subseteq L Γ ⊆ L , the lattice L ∩ span R Γ L \cap\operatorname{span}_{\mathbb{R}}\Gamma L ∩ span R Γ is called the saturation of Γ \Gamma Γ in L L L . It is primitive in L L L and has the same rank as Γ \Gamma Γ .
For L ∈ X n L \in X_n L ∈ X n and 1 ≤ d < n 1 \le d < n 1 ≤ d < n , let Gr ( L , d ) \operatorname{Gr}(L,d) Gr ( L , d ) denote the set of primitive rank-d d d sublattices of L L L .
For a sublattice Γ ⊆ Z n \Gamma\subseteq\mathbb{Z}^{n} Γ ⊆ Z n , define its integral orthogonal complement by
Γ ⊥ Z : = { x ∈ Z n : ⟨ x , γ ⟩ = 0 for every γ ∈ Γ } = ( span R Γ ) ⊥ ∩ Z n . \Gamma^{\perp_{\mathbb{Z}}} := \{x \in\mathbb{Z}^{n} : \langle x,\gamma\rangle= 0 \text{ for every } \gamma\in\Gamma\} = (\operatorname{span}_{\mathbb{R}}\Gamma)^{\perp} \cap\mathbb{Z}^{n}. Γ ⊥ Z := { x ∈ Z n : ⟨ x , γ ⟩ = 0 for every γ ∈ Γ } = ( span R Γ ) ⊥ ∩ Z n .
If Γ ⊆ Z n \Gamma\subseteq\mathbb{Z}^{n} Γ ⊆ Z n is primitive of rank d d d , standard lattice duality [21 ] gives
Γ ⊥ Z is primitive of rank n − d , (95) \Gamma^{\perp_{\mathbb{Z}}} \text{ is primitive of rank } n-d, \tag*{(95)} Γ ⊥ Z is primitive of rank n − d , (95)
det ( Γ ⊥ Z ) = det Γ , (96) \det(\Gamma^{\perp_{\mathbb{Z}}}) = \det\Gamma, \tag*{(96)} det ( Γ ⊥ Z ) = det Γ , (96)
and
( Γ ⊥ Z ) ⊥ Z = Γ . (97) (\Gamma^{\perp_{\mathbb{Z}}})^{\perp_{\mathbb{Z}}} = \Gamma. \tag*{(97)} ( Γ ⊥ Z ) ⊥ Z = Γ. (97)
For a rank-d d d lattice Γ \Gamma Γ and 1 ≤ j ≤ d 1 \le j \le d 1 ≤ j ≤ d , its j j j -th successive minimum λ j ( Γ ) \lambda_j(\Gamma) λ j ( Γ ) is the least λ > 0 \lambda> 0 λ > 0 for which Γ \Gamma Γ contains j j j linearly independent vectors of Euclidean norm at most λ \lambda λ . Thus λ 1 ( Γ ) ≤ ⋯ ≤ λ d ( Γ ) \lambda_1(\Gamma) \le\cdots\le\lambda_d(\Gamma) λ 1 ( Γ ) ≤ ⋯ ≤ λ d ( Γ ) . A standard consequence of the lattice-point estimate in [21 ] is
# ( Γ ∩ [ − R , R ] n ) ≪ n ∏ j = 1 d ( 1 + R λ j ( Γ ) ) , (98) \#(\Gamma\cap[-R,R]^n) \ll_n \prod_{j=1}^{d}\left(1+\frac{R}{\lambda_j(\Gamma)}\right), \tag*{(98)} # ( Γ ∩ [ − R , R ] n ) ≪ n j = 1 ∏ d ( 1 + λ j ( Γ ) R ) , (98)
and Minkowski’s second theorem [2 ] gives
∏ j = 1 d λ j ( Γ ) ≍ n det Γ . (99) \prod_{j=1}^{d}\lambda_j(\Gamma) \asymp_n \det\Gamma. \tag*{(99)} j = 1 ∏ d λ j ( Γ ) ≍ n det Γ. (99)
If λ d ( Γ ) ≪ n R \lambda_d(\Gamma) \ll_n R λ d ( Γ ) ≪ n R , then 1 + R / λ j ( Γ ) ≪ n R / λ j ( Γ ) 1 + R/\lambda_j(\Gamma) \ll_n R/\lambda_j(\Gamma) 1 + R / λ j ( Γ ) ≪ n R / λ j ( Γ ) for every 1 ≤ j ≤ d 1 \le j \le d 1 ≤ j ≤ d . Hence (98) and (99) give
# ( Γ ∩ [ − R , R ] n ) ≪ n R d det Γ . (100) \#(\Gamma\cap[-R,R]^n) \ll_n \frac{R^d}{\det\Gamma}. \tag*{(100)} # ( Γ ∩ [ − R , R ] n ) ≪ n det Γ R d . (100)
Finally, we fix Kim’s notation for the flags appearing below. Let V ( i ) : = π i / 2 / Γ ( 1 + i / 2 ) V(i) := \pi^{i/2}/\Gamma(1+i/2) V ( i ) := π i /2 /Γ ( 1 + i /2 ) be the volume of the unit ball in R i \mathbb{R}^i R i , and adopt the convention ζ ( 1 ) = 1 \zeta(1)=1 ζ ( 1 ) = 1 . For 1 ≤ d < n 1 \le d < n 1 ≤ d < n , define
a ( n , d ) : = 1 n ( n d ) ∏ i = 1 d V ( n − i + 1 ) ζ ( i ) V ( i ) ζ ( n − i + 1 ) . a(n,d) := \frac{1}{n}\binom{n}{d}\prod_{i=1}^{d}\frac{V(n-i+1)\zeta(i)}{V(i)\zeta(n-i+1)}. a ( n , d ) := n 1 ( d n ) i = 1 ∏ d V ( i ) ζ ( n − i + 1 ) V ( n − i + 1 ) ζ ( i ) .
For
d 0 = 0 < d 1 < ⋯ < d k < d k + 1 = n , d = ( d 1 , … , d k ) , d_0=0<d_1<\cdots<d_k<d_{k+1}=n,\qquad\mathbf{d}=(d_1,\ldots,d_k), d 0 = 0 < d 1 < ⋯ < d k < d k + 1 = n , d = ( d 1 , … , d k ) ,
Kim calls a chain
A 1 ⊆ ⋯ ⊆ A k ⊆ L , A i ∈ Gr ( L , d i ) , A_1\subseteq\cdots\subseteq A_k\subseteq L,\qquad A_i\in\operatorname{Gr}(L,d_i), A 1 ⊆ ⋯ ⊆ A k ⊆ L , A i ∈ Gr ( L , d i ) ,
a flag of type d \mathbf{d} d , rational with respect to L L L , and defines
a ( n , d ) : = a ( n , d 1 ) ∏ i = 1 k − 1 n − d i − 1 d i + 1 − d i − 1 a ( n − d i , d i + 1 − d i ) . a(n,\mathbf{d}) := a(n,d_1)\prod_{i=1}^{k-1}\frac{n-d_{i-1}}{d_{i+1}-d_{i-1}}a(n-d_i,d_{i+1}-d_i). a ( n , d ) := a ( n , d 1 ) i = 1 ∏ k − 1 d i + 1 − d i − 1 n − d i − 1 a ( n − d i , d i + 1 − d i ) .
We now record the only non-elementary input.
Theorem B.2 (Kim’s mean value formula for rational flags [12 ] ). For H 1 , … , H k ≥ 0 H_1,\ldots,H_k\ge0 H 1 , … , H k ≥ 0 , the μ n \mu_n μ n -average of the number of flags A 1 ⊆ ⋯ ⊆ A k A_1\subseteq\cdots\subseteq A_k A 1 ⊆ ⋯ ⊆ A k of type d \mathbf{d} d , rational with respect to L L L , such that det A i ≤ H i \det A_i\le H_i det A i ≤ H i for i = 1 , … , k i=1,\ldots,k i = 1 , … , k , is equal to a ( n , d ) ∏ i = 1 k H i d i + 1 − d i − 1 a(n,\mathbf{d})\prod_{i=1}^{k}H_i^{d_{i+1}-d_{i-1}} a ( n , d ) ∏ i = 1 k H i d i + 1 − d i − 1 .
We transfer the case k = 2 k=2 k = 2 from a random unimodular lattice to the fixed lattice Z n \mathbb{Z}^n Z n .
Lemma B.3 (Two-height flag count). Fix positive integers n , d 1 , d 2 n,d_1,d_2 n , d 1 , d 2 with 0 < d 1 < d 2 < n 0<d_1<d_2<n 0 < d 1 < d 2 < n . Uniformly for H 1 , H 2 ≥ 1 H_1,H_2\ge1 H 1 , H 2 ≥ 1 ,
# { ( A 1 , A 2 ) : A 1 ∈ Gr ( Z n , d 1 ) , A 2 ∈ Gr ( Z n , d 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 } ≪ n , d 1 , d 2 H 1 d 2 H 2 n − d 1 . \#\left\{(A_1,A_2):
\begin{array}{l}
A_1\in\operatorname{Gr}(\mathbb{Z}^n,d_1),\\
A_2\in\operatorname{Gr}(\mathbb{Z}^n,d_2),\\
A_1\subseteq A_2,\\
\det A_1\le H_1,\ \det A_2\le H_2
\end{array}
\right\}
\ll_{n,d_1,d_2} H_1^{d_2}H_2^{n-d_1}. # ⎩ ⎨ ⎧ ( A 1 , A 2 ) : A 1 ∈ Gr ( Z n , d 1 ) , A 2 ∈ Gr ( Z n , d 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 ⎭ ⎬ ⎫ ≪ n , d 1 , d 2 H 1 d 2 H 2 n − d 1 .
Proof. Take k = 2 k=2 k = 2 and d = ( d 1 , d 2 ) \mathbf{d}=(d_1,d_2) d = ( d 1 , d 2 ) in Theorem B.2. Since d 0 = 0 d_0=0 d 0 = 0 and d 3 = n d_3=n d 3 = n , it gives
∫ X n # { ( A 1 , A 2 ) : A 1 ∈ Gr ( L , d 1 ) , A 2 ∈ Gr ( L , d 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 } d μ n ( L ) = a ( n , d ) H 1 d 2 − d 0 H 2 d 3 − d 1 = a ( n , d ) H 1 d 2 H 2 n − d 1 . (101) \begin{aligned}
\int_{X_n}\#\left\{(A_1,A_2):
\begin{array}{l}
A_1\in\operatorname{Gr}(L,d_1),\\
A_2\in\operatorname{Gr}(L,d_2),\\
A_1\subseteq A_2,\\
\det A_1\le H_1,\ \det A_2\le H_2
\end{array}
\right\}\,d\mu_n(L)
= a(n,\mathbf{d})H_1^{d_2-d_0}H_2^{d_3-d_1} \\
&= a(n,\mathbf{d})H_1^{d_2}H_2^{n-d_1}. \tag*{(101)}
\end{aligned} ∫ X n # ⎩ ⎨ ⎧ ( A 1 , A 2 ) : A 1 ∈ Gr ( L , d 1 ) , A 2 ∈ Gr ( L , d 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 ⎭ ⎬ ⎫ d μ n ( L ) = a ( n , d ) H 1 d 2 − d 0 H 2 d 3 − d 1 = a ( n , d ) H 1 d 2 H 2 n − d 1 . (101)
It remains to pass from this average to the particular lattice Z n \mathbb{Z}^n Z n . Let π : SL ( n , R ) ⟶ X n \pi:\operatorname{SL}(n,\mathbb{R})\longrightarrow X_n π : SL ( n , R ) ⟶ X n be the quotient map. Choose a relatively compact open neighborhood W W W of the identity on which π \pi π is injective, and put U = π ( W ) U=\pi(W) U = π ( W ) . Then μ n ( U ) > 0 \mu_n(U)>0 μ n ( U ) > 0 . For A ⊆ Z n A\subseteq\mathbb{Z}^n A ⊆ Z n and g ∈ SL ( n , R ) g\in\operatorname{SL}(n,\mathbb{R}) g ∈ SL ( n , R ) , write A g : = { a g : a ∈ A } Ag := \{ag : a \in A\} A g := { a g : a ∈ A } . For 1 ≤ j < n 1 \le j < n 1 ≤ j < n , the relative compactness of W W W gives a constant c j < ∞ c_j < \infty c j < ∞ such that
det ( A g ) ≤ c j det A ( A ∈ Gr ( Z n , j ) , g ∈ W ) . \det(Ag) \le c_j \det A \qquad(A \in\operatorname{Gr}(\mathbb{Z}^n,j),\ g \in W). det ( A g ) ≤ c j det A ( A ∈ Gr ( Z n , j ) , g ∈ W ) .
Right multiplication by g g g maps Gr ( Z n , j ) \operatorname{Gr}(\mathbb{Z}^n,j) Gr ( Z n , j ) bijectively onto Gr ( Z n g , j ) \operatorname{Gr}(\mathbb{Z}^n g,j) Gr ( Z n g , j ) and preserves inclusions. Hence, for every g ∈ W g \in W g ∈ W ,
# { ( A 1 , A 2 ) : A i ∈ Gr ( Z n , d i ) ( i = 1 , 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 } ≤ # { ( A 1 , A 2 ) : A i ∈ Gr ( Z n g , d i ) ( i = 1 , 2 ) , A 1 ⊆ A 2 , det A 1 ≤ c d 1 H 1 , det A 2 ≤ c d 2 H 2 } . \#\left\{(A_1,A_2):
\begin{array}{l}
A_i \in\operatorname{Gr}(\mathbb{Z}^n,d_i)\quad(i=1,2),\\
A_1 \subseteq A_2,\\
\det A_1 \le H_1,\quad\det A_2 \le H_2
\end{array}
\right\}
\le
\#\left\{(A_1,A_2):
\begin{array}{l}
A_i \in\operatorname{Gr}(\mathbb{Z}^n g,d_i)\quad(i=1,2),\\
A_1 \subseteq A_2,\\
\det A_1 \le c_{d_1}H_1,\\
\det A_2 \le c_{d_2}H_2
\end{array}
\right\}. # ⎩ ⎨ ⎧ ( A 1 , A 2 ) : A i ∈ Gr ( Z n , d i ) ( i = 1 , 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 ⎭ ⎬ ⎫ ≤ # ⎩ ⎨ ⎧ ( A 1 , A 2 ) : A i ∈ Gr ( Z n g , d i ) ( i = 1 , 2 ) , A 1 ⊆ A 2 , det A 1 ≤ c d 1 H 1 , det A 2 ≤ c d 2 H 2 ⎭ ⎬ ⎫ .
Integrating this inequality over U U U and applying (101) with ( H 1 , H 2 ) (H_1,H_2) ( H 1 , H 2 ) replaced by ( c d 1 H 1 , c d 2 H 2 ) (c_{d_1}H_1,c_{d_2}H_2) ( c d 1 H 1 , c d 2 H 2 ) , we obtain
μ n ( U ) # { ( A 1 , A 2 ) : A i ∈ Gr ( Z n , d i ) ( i = 1 , 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 } ≤ a ( n , d ) ( c d 1 H 1 ) d 2 ( c d 2 H 2 ) n − d 1 . \mu_n(U)\#
\left\{(A_1,A_2):
\begin{array}{l}
A_i \in\operatorname{Gr}(\mathbb{Z}^n,d_i)\quad(i=1,2),\\
A_1 \subseteq A_2,\\
\det A_1 \le H_1,\quad\det A_2 \le H_2
\end{array}
\right\}
\le a(n,\mathbf d)(c_{d_1}H_1)^{d_2}(c_{d_2}H_2)^{n-d_1}. μ n ( U ) # ⎩ ⎨ ⎧ ( A 1 , A 2 ) : A i ∈ Gr ( Z n , d i ) ( i = 1 , 2 ) , A 1 ⊆ A 2 , det A 1 ≤ H 1 , det A 2 ≤ H 2 ⎭ ⎬ ⎫ ≤ a ( n , d ) ( c d 1 H 1 ) d 2 ( c d 2 H 2 ) n − d 1 .
Since W W W is fixed in terms of n n n , this proves the result. □ \square □
Proof of the matrix count
Proof of Lemma B.1. Decompose the count according to
d 1 = rank U , d 2 = n − rank V . d_1 = \operatorname{rank} U,\qquad d_2 = n-\operatorname{rank} V. d 1 = rank U , d 2 = n − rank V .
If d 1 = 0 d_1=0 d 1 = 0 , then U = 0 U=0 U = 0 , and there are O ( R n t ) O(R^{nt}) O ( R n t ) choices for V V V . This is at most the asserted bound because
n ( s + t ) − 2 s t − n t = s ( n − 2 t ) ≥ 0. n(s+t)-2st-nt=s(n-2t)\ge0. n ( s + t ) − 2 s t − n t = s ( n − 2 t ) ≥ 0.
The case d 2 = n d_2=n d 2 = n , in which V = 0 V=0 V = 0 , is symmetric. We may therefore assume
1 ≤ d 1 ≤ s , n − t ≤ d 2 ≤ n − 1. 1\le d_1\le s,\qquad n-t\le d_2\le n-1. 1 ≤ d 1 ≤ s , n − t ≤ d 2 ≤ n − 1.
Since d 1 + ( n − d 2 ) ≤ s + t < n d_1+(n-d_2)\le s+t<n d 1 + ( n − d 2 ) ≤ s + t < n , we have d 1 < d 2 d_1<d_2 d 1 < d 2 .
The rows of U U U generate a sublattice of Z n \mathbb{Z}^n Z n . Its saturation is the intersection of Z n \mathbb{Z}^n Z n with the real span of those rows. Define this saturated row lattice, and its analogue for V V V , by
Γ U : = rowspan R ( U ) ∩ Z n , Γ V : = rowspan R ( V ) ∩ Z n . \Gamma_U:=\operatorname{rowspan}_{\mathbb R}(U)\cap\mathbb{Z}^n,\qquad
\Gamma_V:=\operatorname{rowspan}_{\mathbb R}(V)\cap\mathbb{Z}^n. Γ U := rowspan R ( U ) ∩ Z n , Γ V := rowspan R ( V ) ∩ Z n .
Here rowspan R \operatorname{rowspan}_{\mathbb R} rowspan R denotes the real span of the rows. The lattices Γ U \Gamma_U Γ U and Γ V \Gamma_V Γ V are primitive of ranks d 1 d_1 d 1 and n − d 2 n-d_2 n − d 2 , respectively. The equation U V T = 0 UV^{\mathsf T}=0 U V T = 0 says that their real row spaces are orthogonal. Consequently, if Λ V : = Γ V ⊥ Z \Lambda_V:=\Gamma_V^{\perp_{\mathbb Z}} Λ V := Γ V ⊥ Z , then Γ U ⊆ Λ V \Gamma_U\subseteq\Lambda_V Γ U ⊆ Λ V , and (95) shows that Λ V \Lambda_V Λ V is primitive of rank d 2 d_2 d 2 . Thus
Γ U ⊆ Λ V ⊆ Z n (102) \Gamma_U\subseteq\Lambda_V\subseteq\mathbb{Z}^n \tag*{(102)} Γ U ⊆ Λ V ⊆ Z n (102)
is a flag of type ( d 1 , d 2 ) (d_1,d_2) ( d 1 , d 2 ) , rational with respect to Z n \mathbb{Z}^n Z n . Moreover, (96) gives det Λ V = det Γ V \det\Lambda_V=\det\Gamma_V det Λ V = det Γ V , while (97) gives Γ V = Λ V ⊥ Z \Gamma_V=\Lambda_V^{\perp_{\mathbb Z}} Γ V = Λ V ⊥ Z . Thus the flag in (102) determines the pair ( Γ U , Γ V ) (\Gamma_U,\Gamma_V) ( Γ U , Γ V ) .
We next count the matrices giving a fixed pair of row lattices. Since U U U has rank d 1 d_1 d 1 , it has d 1 d_1 d 1 linearly independent rows, each of Euclidean norm O n ( R ) O_n(R) O n ( R ) . Hence λ d 1 ( Γ U ) ≪ n R \lambda_{d_1}(\Gamma_U)\ll_n R λ d 1 ( Γ U ) ≪ n R . Moreover, the lattice generated by these d 1 d_1 d 1 rows has determinant O n ( R d 1 ) O_n(R^{d_1}) O n ( R d 1 ) by Hadamard’s inequality. Since $\Gamma_U is its saturation, (93) shows that saturation can only decrease determinant. Together with (94) , this gives 1 ≤ det Γ U ≪ n R d 1 1 \le\det\Gamma_U \ll_n R^{d_1} 1 ≤ det Γ U ≪ n R d 1 . The same argument gives
λ n − d 2 ( Γ V ) ≪ n R , 1 ≤ det Γ V ≪ n R n − d 2 . \lambda_{n-d_2}(\Gamma_V) \ll_n R,\qquad1 \le\det\Gamma_V \ll_n R^{n-d_2}. λ n − d 2 ( Γ V ) ≪ n R , 1 ≤ det Γ V ≪ n R n − d 2 .
It follows from (100) that, once Γ U \Gamma_U Γ U and Γ V \Gamma_V Γ V are fixed, the numbers of possible matrices are bounded by
# { U } ≪ n , s ( R d 1 det Γ U ) s , # { V } ≪ n , t ( R n − d 2 det Γ V ) t . (103) \#\{U\} \ll_{n,s} \left(\frac{R^{d_1}}{\det\Gamma_U}\right)^s,\qquad\#\{V\} \ll_{n,t} \left(\frac{R^{n-d_2}}{\det\Gamma_V}\right)^t. \tag*{(103)} # { U } ≪ n , s ( det Γ U R d 1 ) s , # { V } ≪ n , t ( det Γ V R n − d 2 ) t . (103)
Let N d 1 , d 2 ( R ) N_{d_1,d_2}(R) N d 1 , d 2 ( R ) denote the contribution from matrices with these fixed ranks. Choose integers J 1 , J 2 ≥ 0 J_1,J_2 \ge0 J 1 , J 2 ≥ 0 such that all possible determinants are covered by the dyadic ranges
2 j ≤ det Γ U < 2 j + 1 ( 0 ≤ j ≤ J 1 ) , 2 ℓ ≤ det Γ V < 2 ℓ + 1 ( 0 ≤ ℓ ≤ J 2 ) , 2^j \le\det\Gamma_U < 2^{j+1}\qquad(0 \le j \le J_1),\qquad2^\ell\le\det\Gamma_V < 2^{\ell+1}\qquad(0 \le\ell\le J_2), 2 j ≤ det Γ U < 2 j + 1 ( 0 ≤ j ≤ J 1 ) , 2 ℓ ≤ det Γ V < 2 ℓ + 1 ( 0 ≤ ℓ ≤ J 2 ) ,
with
2 J 1 ≍ n R d 1 , 2 J 2 ≍ n R n − d 2 . 2^{J_1} \asymp_n R^{d_1},\qquad2^{J_2} \asymp_n R^{n-d_2}. 2 J 1 ≍ n R d 1 , 2 J 2 ≍ n R n − d 2 .
For fixed j j j and ℓ \ell ℓ , the orthogonal-complement identity and Lemma B.3, applied with heights 2 j + 1 2^{j+1} 2 j + 1 and 2 ℓ + 1 2^{\ell+1} 2 ℓ + 1 , show that the number of possible pairs ( Γ U , Γ V ) (\Gamma_U,\Gamma_V) ( Γ U , Γ V ) is
≪ n , d 1 , d 2 2 j d 2 2 ℓ ( n − d 1 ) . \ll_{n,d_1,d_2} 2^{jd_2}2^{\ell(n-d_1)}. ≪ n , d 1 , d 2 2 j d 2 2 ℓ ( n − d 1 ) .
For each such pair, (103) bounds the number of matrices by
≪ n , s , t R d 1 s + ( n − d 2 ) t 2 − j s 2 − ℓ t . \ll_{n,s,t} R^{d_1s+(n-d_2)t}2^{-js}2^{-\ell t}. ≪ n , s , t R d 1 s + ( n − d 2 ) t 2 − j s 2 − ℓ t .
Thus
N d 1 , d 2 ( R ) ≪ n , s , t R d 1 s + ( n − d 2 ) t ∑ j = 0 J 1 ∑ ℓ = 0 J 2 2 j ( d 2 − s ) 2 ℓ ( n − d 1 − t ) = R d 1 s + ( n − d 2 ) t ( ∑ j = 0 J 1 2 j ( d 2 − s ) ) ( ∑ ℓ = 0 J 2 2 ℓ ( n − d 1 − t ) ) \begin{aligned}
N_{d_1,d_2}(R) \ll_{n,s,t} R^{d_1s+(n-d_2)t}\sum_{j=0}^{J_1}\sum_{\ell=0}^{J_2}2^{j(d_2-s)}2^{\ell(n-d_1-t)} \\
&=R^{d_1s+(n-d_2)t}\left(\sum_{j=0}^{J_1}2^{j(d_2-s)}\right)\left(\sum_{\ell=0}^{J_2}2^{\ell(n-d_1-t)}\right)
\end{aligned} N d 1 , d 2 ( R ) ≪ n , s , t R d 1 s + ( n − d 2 ) t j = 0 ∑ J 1 ℓ = 0 ∑ J 2 2 j ( d 2 − s ) 2 ℓ ( n − d 1 − t ) = R d 1 s + ( n − d 2 ) t ( j = 0 ∑ J 1 2 j ( d 2 − s ) ) ( ℓ = 0 ∑ J 2 2 ℓ ( n − d 1 − t ) )
≪ n , s , t R d 1 s + ( n − d 2 ) t 2 J 1 ( d 2 − s ) 2 J 2 ( n − d 1 − t ) \ll_{n,s,t} R^{d_1s+(n-d_2)t}2^{J_1(d_2-s)}2^{J_2(n-d_1-t)} ≪ n , s , t R d 1 s + ( n − d 2 ) t 2 J 1 ( d 2 − s ) 2 J 2 ( n − d 1 − t )
≪ n , s , t R d 1 s + ( n − d 2 ) t R d 1 ( d 2 − s ) R ( n − d 2 ) ( n − d 1 − t ) = R d 1 s + ( n − d 2 ) t + d 1 d 2 − d 1 s + ( n − d 2 ) ( n − d 1 ) − ( n − d 2 ) t = R d 1 d 2 + ( n − d 2 ) ( n − d 1 ) = R n 2 − n ( d 1 + d 2 ) + 2 d 1 d 2 . \begin{aligned}
\ll_{n,s,t} R^{d_1s+(n-d_2)t}R^{d_1(d_2-s)}R^{(n-d_2)(n-d_1-t)} \\
&=R^{d_1s+(n-d_2)t+d_1d_2-d_1s+(n-d_2)(n-d_1)-(n-d_2)t} \\
&=R^{d_1d_2+(n-d_2)(n-d_1)} \\
&=R^{n^2-n(d_1+d_2)+2d_1d_2}.
\end{aligned} ≪ n , s , t R d 1 s + ( n − d 2 ) t R d 1 ( d 2 − s ) R ( n − d 2 ) ( n − d 1 − t ) = R d 1 s + ( n − d 2 ) t + d 1 d 2 − d 1 s + ( n − d 2 ) ( n − d 1 ) − ( n − d 2 ) t = R d 1 d 2 + ( n − d 2 ) ( n − d 1 ) = R n 2 − n ( d 1 + d 2 ) + 2 d 1 d 2 .
It remains to maximize the exponent
E ( d 1 , d 2 ) : = n 2 − n ( d 1 + d 2 ) + 2 d 1 d 2 E(d_1,d_2):=n^2-n(d_1+d_2)+2d_1d_2 E ( d 1 , d 2 ) := n 2 − n ( d 1 + d 2 ) + 2 d 1 d 2
over 1 ≤ d 1 ≤ s 1 \le d_1 \le s 1 ≤ d 1 ≤ s and n − t ≤ d 2 ≤ n − 1 n-t \le d_2 \le n-1 n − t ≤ d 2 ≤ n − 1 . We have
E ( d 1 + 1 , d 2 ) − E ( d 1 , d 2 ) = 2 d 2 − n ≥ n − 2 t ≥ 0 E(d_1+1,d_2)-E(d_1,d_2)=2d_2-n\ge n-2t\ge0 E ( d 1 + 1 , d 2 ) − E ( d 1 , d 2 ) = 2 d 2 − n ≥ n − 2 t ≥ 0
and
E ( d 1 , d 2 − 1 ) − E ( d 1 , d 2 ) = n − 2 d 1 ≥ n − 2 s ≥ 0. E(d_1,d_2-1)-E(d_1,d_2)=n-2d_1\ge n-2s\ge0. E ( d 1 , d 2 − 1 ) − E ( d 1 , d 2 ) = n − 2 d 1 ≥ n − 2 s ≥ 0.
Thus E ( d 1 , d 2 ) E(d_1,d_2) E ( d 1 , d 2 ) is maximized at d 1 = s d_1=s d 1 = s and d 2 = n − t d_2=n-t d 2 = n − t , where
E ( s , n − t ) = n ( s + t ) − 2 s t . E(s,n-t)=n(s+t)-2st. E ( s , n − t ) = n ( s + t ) − 2 s t .
Summing over the O n ( 1 ) O_n(1) O n ( 1 ) choices for ( d 1 , d 2 ) (d_1,d_2) ( d 1 , d 2 ) completes the proof.