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The Carleson–Hunt Time–Frequency Theorem

1 · Prerequisites

2 · Summary

The argument establishes the Fourier partial-sum maximal inequality on the circle for every exponent strictly between one and infinity. It begins with one-sided real-line cutoffs and an explicit nonzero Schwartz packet, then proves uniform bounds for finite linearised tile sums before reconstructing the continuous operator and transferring it to the circle.

Tiles use half-open dyadic intervals of reciprocal lengths. Density tests all dominating tiles; size ranges over plus subtrees with designated tops; forest count retains top multiplicity. Density and size selection have explicit packing proofs. The single-tree estimate supplies the spatial tails, sparse testing support and exact smooth frequency truncations. Their joint stopping decomposition gives the weak-L2 testing bound.

The all-exponent argument uses a localized signed-tree weak-(1,1) estimate proved by a modulated kernel decomposition. Finite random signs and a subtree stopping argument control the square function. The exceptional-set decomposition gives Hunt's logarithmic major-subset bound, and a direct restricted weak interpolation argument yields strong finite-model estimates on both sides of exponent two and at every other interior exponent.

Averages over common spatial and frequency periods have an explicit multiplier. Logarithmic scale averaging turns it into a positive constant times the one-sided Fourier cutoff, with all index and parameter limits justified. The strong real-line L2 operator extends by a Lipschitz bound and Schwartz density. For the torus, a slowly scaled packet localizes a trigonometric polynomial; half-integer cutoffs select its integer modes exactly. Periodic averaging and Fejer approximation preserve normalized Haar measure and give the maximal inequality for all complex Lp inputs.

AC is explicit through the Fourier and measure-theoretic suppliers. The positive bounds concern only 1<p<infinity; the earlier Kolmogorov construction supplies the obstruction at p=1.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-5.6-terra)Open item page →

Carleson operator and measurable linearisation

Definition

Assume The Axiom of Choice for the countable-choice hypotheses of the Schwartz Fourier and integrability suppliers. Use the transform f^(ξ)=f(x)e2πixξdx of Fourier transform on complex L1 classes, with absolute convergence and representative independence from The integral transform is representative independent. For fS(R) as in Schwartz space and its seminorms, Fourier transform acts continuously on Schwartz space and Schwartz derivatives are integrable give f,f^L1. Define Taf(x)=af^(ξ)e2πixξdξ,CRf(x)=supaRTaf(x). These integrals converge absolutely at every x and satisfy Taf(x)f^1. The cutoff function is continuous in a: since f^ is bounded, Taf(x)Tbf(x)f^ab. Hence the supremum equals the supremum over rational a.

For fixed a, Taf is continuous in x. To check this without an unproved interchange, first truncate its absolutely integrable frequency tail beyond ξ>R to make that tail contribution to any difference less than a prescribed epsilon. On the remaining interval, e2πixξe2πiyξ2πRxy, so the integral difference is bounded by 2πRxyf^1. Thus the countable rational supremum is measurable.

For a nonempty finite list of rational cutoffs q1,,qM, put CMf=max1jMTqjf. Choose the least maximizing index j(x); its level sets are finite intersections of measurable comparison sets, so af(x)=qj(x) is measurable. Then CMf(x)=Taf(x)f(x). For a fixed measurable selector a, the map fTa(x)f(x) is linear; when the selector was chosen from f, it is frozen before invoking a uniform operator estimate. No single selector is claimed to attain the unrestricted real supremum.

For h(x)=Taf(x)f(x) put u(x)=h(x)/h(x) when h(x)0 and u(x)=1 otherwise. This is a measurable unimodular phase and hu=h. Thus, on a measurable finite-measure testing set E, pairing h with u1E gives Eh. The phase at zero is fixed explicitly. All finite selectors use least-index rules; AC is inherited from the stated analytic suppliers, not from selecting maximizers.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-5.6-terra)Open item page →

Carleson tiles wave packets and tile order

Definition

Assume The Axiom of Choice for the countable-choice Fourier suppliers. A dyadic interval is [j2k,(j+1)2k) with j,kZ; its length is positive. A tile is s=Is×ωs with both intervals dyadic and Isωs=1. Their centers are denoted c. Write ωs, and ωs,+ for the left and right half-open halves. The midpoint belongs only to the right half.

Here is an explicit nonzero packet convention. Define ρ(t)=exp(1/t) for t>0 and ρ(t)=0 for t0. Here is the complete smoothness justification. Put P0(u)=1 and recursively Pk+1(u)=u2(Pk(u)Pk(u)). The exponential derivative, chain rule and product/quotient rules (The exponential function is smooth and (exp)=exp, The chain rule, in one line from Carathéodory: if g is differentiable at c and f is differentiable at g(c), then fg is differentiable at c with (fg)(c)=f(g(c))g(c), Sums, scalar multiples, products and quotients: (f+g)(c)=f(c)+g(c), (αf)(c)=αf(c), (fg)(c)=f(c)g(c)+f(c)g(c), and (f/g)(c)=(f(c)g(c)f(c)g(c))/g(c)2 when g(c)0) show on t>0 that the kth derivative is Pk(1/t)exp(1/t). For each polynomial P and each integer r>=0, trP(1/t)exp(1/t)0 as t0: expand P into finitely many monomials, put u=1/t, and apply The exponential dominates every fixed nonnegative integer power at + with parameter one and The exponential is positive and satisfies exp(x)=1/exp(x). Extend each displayed kth-derivative formula by zero on t0. Each extension is continuous at zero (r=0); its difference quotient at zero tends to zero (r=1), so its derivative there equals the next extension's value. On the two open half-lines differentiation already gives the next extension. Induction therefore proves ρC(R) and ρ(k)(0)=0 for every k. Positivity of the exponential makes ρ(t)>0 exactly when t>0. Set a=1/9, b=1/8 and ψ(ξ)=ρ(b2ξ2)ρ(b2ξ2)+ρ(ξ2a2). The denominator is positive: for ξa its first term is positive, for ξb its second term is positive, and between a and b both are positive. Thus ψ is smooth, 0ψ1, equals one on [a,a] and vanishes outside [b,b]. Every derivative is bounded on its compact support, so it is Schwartz under Schwartz space and its seminorms. Define ϕ(x)=ψ^(x). The transform theorem Fourier transform acts continuously on Schwartz space makes this Schwartz, and Fourier inversion on Schwartz space gives ϕ^=ψ. In detail inversion applied to ψ says ψ^(y)e2πiyξdy=ψ(ξ); changing y=x gives the claimed transform. All integrals are absolute by Schwartz derivatives are integrable. In particular ϕ is not zero. No unspecified zero packet is allowed.

Use Tryh(x)=h(xy), Modξh(x)=e2πiξxh(x) and Dil2h(x)=1/2h(x/) for >0. Define ϕs(x)=Is1/2e2πic(ωs,)xϕ((xc(Is))/Is). The translation, modulation and dilation laws Translation, modulation, linear dilation and reflection laws give ϕs^(ξ)=Is1/2e2πic(Is)(ξc(ωs,))ψ(Is(ξc(ωs,))). Its support lies strictly inside ωs,, since its half-width is 1/(8Is) whereas that half-interval has half-width 1/(4Is). For every integer M there is a finite constant CM with ϕs(x)CMIs1/2(1+xc(Is)/Is)M, directly from the Schwartz seminorms. The pairing is f,h=fh, linear in f. The fixed packet is not assumed to have norm one; its fixed norm and seminorms enter constants.

Set st when IsIt and ωtωs. This is a partial order: reflexivity and transitivity follow from inclusions, and mutual comparability gives equality of both intervals. A finite tree is a set T with a designated top tile t such that st for every sT; the top need not belong to T and is part of the data. A plus tree has, in addition, ωtωs,+ for every sT{t}. The top alone is allowed. Empty trees have no contribution; any assigned top is retained only when a forest count is explicitly specified. For a finite tile set S, a measurable selector N:RR and fL2, define the finite model CS,Nf(x)=sSf,ϕs1ωs,+(N(x))ϕs(x). The coefficients exist by Cauchy–Schwarz The complex L2 pairing is well-defined and satisfies Cauchy–Schwarz and Schwartz integrability. Each summand is measurable, the sum is finite and for fixed S,N it is linear in f. Its testing form for g1E, m(E)<, is the sum of f,ϕs1ωs,+(N)ϕs,g; these integrals exist since the packets are integrable and g is bounded. These definitions do not assert orthogonality of overlapping packets.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Wave packet model dominates the linearised carleson operator

Statement

Assume AC and let 1<p<. If every finite linearised tile model has strong type (p,p) bound B, uniformly in its finite family and measurable selector, then for Schwartz input CRfpκ1Bfp. If instead these finite models have a uniform weak type (p,p) bound B, then m{CRf>λ}(pκ1Bfp/λ)p(λ>0),p=p/(p1). The same conclusions hold when the hypothesis is formulated for all translated and reciprocally dilated grids. Such grid bounds already follow from the original-grid bounds by conjugating translations, modulations and dilations. The fixed reconstruction constant is κ=1log20H(t)dtt>0,H(t)=1/43/4ψ(ut)2du, where psi is the exact fixed packet transform. This transfers in particular weak L2 and every strong Lp bound needed for the real-line Carleson theorem. No pointwise bound by one unaveraged model is asserted.

Facts & Assumptions

[F1]

One-sided Schwartz Fourier cutoffs are absolutely defined, continuous in the cutoff parameter, and finite rational maxima have measurable least-index linearising selectors Carleson operator and measurable linearisation.

[F2]

The exact packets, grid order, finite models, nonnegative even transform psi, plateau a=1/9 and support b=1/8 have the stated conventions Carleson tiles wave packets and tile order.

[F3]

Schwartz convolution has transform equal to the product of the transforms Schwartz convolution and product laws.

[F4]

Schwartz Fourier inversion holds everywhere Fourier inversion on Schwartz space.

[F5]

Tonelli applies to nonnegative product-measurable functions on sigma-finite products Tonelli's theorem for nonnegative measurable functions on a sigma-finite product.

[F6]

Fubini applies to absolutely integrable functions on sigma-finite products Fubini's theorem for L^1 functions on a sigma-finite product.

[F7]

Dominated convergence passes integrals to almost-everywhere limits under one integrable majorant Dominated convergence.

[F8]

Monotone convergence passes increasing nonnegative integrands to their integral limit Monotone convergence for the integral.

[F9]

Complex Hölder and Minkowski hold Complex Holder, Minkowski, and the quotient norm.

[F11]

Assume AC The Axiom of Choice, supplying the countable-choice Fourier and measure interfaces.

Proof

Given: The stated uniform finite-model bound, a Schwartz input f, and an arbitrary measurable real selector N. All averages below use probability measures, and all product spaces are Euclidean Lebesgue spaces, finite parameter intervals or countable counting spaces, hence sigma-finite.

1.1

A transformed grid has spatial intervals y+rI and frequency intervals η+r1ω for r>0 and fixed y,eta. Use the same packet formula at their actual centers and lengths. Let Uf(x)=r1/2e2πiηxf((xy)/r). Substitution in the packet formula shows that U sends each original packet to its transformed packet times a unimodular constant; that constant cancels in its coefficient times packet. Thus the transformed model is UCS,NU1, where N(z)=r(N(y+rz)η). This selector is measurable. The Lp norm of U is the scalar factor r1/p1/2, and that of its inverse is reciprocal, so the strong norm B is preserved. The distribution change of variables gives the same assertion for the weak norm. Therefore the hypothesis covers every transformed grid used below.

F1F2F9given
1.2

A weak bound m{v>t}(K/t)p implies EvpKm(E)11/p for each finite-measure E: by F10 with exponent one, its left side is at most 0min(m(E),(K/t)p)dt, which equals the stated expression after splitting at Km(E)1/p. The zero cases give zero directly. Consequently the weak model hypothesis implies the testing bound Cf,gpBfpm(E)11/p for g1E. The strong hypothesis implies Cf,gBfpgp by F9. We will preserve these two testing inequalities through probability averages, first for bounded g supported in a bounded interval.

F9F10given
2.1

Fix an integer K>=0 and r in [1,2]. Retain only spatial scales l=r2k, -K<=k<=K. Average transformed grids over 0y<Y=r2K and 0η<W=2K/r, with normalized measures dy/Y and deta/W. At each fixed scale retain initially only spatial indices i and frequency indices j with |i|,|j|<=M, so the operator is a finite model covered by steps 1.1 and 1.2. Let M increase. For each x and each scale, the upper-half selector condition activates at most one frequency index. Every coefficient has magnitude at most f2ϕ2 by F9, and the sum of the packet absolute values over its spatial lattice is at most Cl1/2 by Schwartz decay. Thus all truncated outputs and their pointwise limits are bounded by CKf2, uniformly in x,y,eta,r. The spatial series is absolutely convergent, and the unique active frequency index is eventually retained, so the limits exist for every x and parameter. Coefficients are continuous in the packet parameters, by domination on each compact parameter set; the selector indicators are measurable in x and the parameters. Hence all the functions being averaged are product-measurable. For bounded compactly supported g, F7 and this uniform bound pass both testing inequalities through M to the full-index, finite-scale average.

F2F7F9step 1.1step 1.2
3.1

We compute its spatial average exactly. Fix l and a frequency interval with lower center nu. Write pl,c,ν(x)=l1/2e2πiνxϕ((xc)/l). The spatial centers are c=y+l(i+1/2). Since Y/l is an integer, averaging their lattice over y gives density dc/l on the line. Therefore the averaged rank-one sum has kernel 1le2πiν(xz)R((xz)/l),R(v)=ϕ(v+u)ϕ(u)du. Indeed insert the coefficient integral and replace the averaged sum over c by l^(-1) times its integral; then substitute c=z-lu. These exchanges are absolute: at fixed x, the integral over z,c of the absolute integrand after averaging is at most l1f1ϕϕ1. F5 establishes absolute product integrability and F6 exchanges the integrals and the counting sum. Since psi is real and even, its inverse phi is real and even by the cosine integral, so R=phi*phi and F3 gives R^=ψ2. F3 and F4 now identify the averaged output as f^(ξ)e2πixξψ(l(ξν))2dξ. All these are Schwartz convolution identities at a fixed frequency.

F2F3F4F5F6step 2.1
4.1

Average next over eta. For a fixed x let a=N(x). At spatial length l, the frequency intervals have length1/l. The interval containing a is active exactly when the fractional position v=l(aη)j lies in [1/2,1). Its lower center is ν=η+(j+1/4)/l, so l(ξν)=l(ξa)+v1/4. Because W is an integer multiple of1/l, the eta average makes v uniform on [0,1]. It follows that the frequency-averaged multiplier at scale l is 1/21ψ(l(ξa)+v1/4)2dv=H(l(aξ)). The half-open endpoints make a unique interval convention; their eta measure is zero and they do not alter the integral. Since 0ψ1 and f^L1, the Fourier integral and finite eta average interchange absolutely by F6. Crucially, this calculation holds at every fixed x for its actual value N(x); N is not frozen to a constant across the spatial variable.

F1F2F6step 3.1
5.1

Finally average r over [1,2] with probability measure dr/(rlog2). The resulting finite-scale average AK,Nf has the exact representation AK,Nf(x)=f^(ξ)e2πixξmK(N(x)ξ)dξ,mK(d)=1log22K2K+1H(td)dtt. This follows by changing variables t=r2^k in the finite scale sum: the intervals [2^k,2^(k+1)] partition the displayed integration range. The measure in r and the normalized measures in y,eta form a probability measure; they can equivalently be parameterized by y/Y,eta/W in [0,1]. Thus step 2.1 passes the testing bounds to AK,Nf. All integrations here are on compact parameter ranges with the stated uniform majorant.

F6step 2.1step 4.1
6.1

The function H is nonnegative, bounded by1/2, and supported in [1/8,7/8]. Moreover H(t)2/9 for 3/8t5/8: the interval [t1/9,t+1/9] lies inside [1/4,3/4] and its integrand is one. Consequently 0<2log(5/3)9log2κlog72log2<. For d<=0, m_K(d)=0. For d>0, the substitution u=td and expansion of the integration range give mK(d)κ. Thus 0mKκ for every K. Since f^L1, F7 gives, pointwise for every finite N(x), AK,Nf(x)κN(x)f^(ξ)e2πixξdξ=κTN(x)f(x). The isolated frequency xi=N(x) has zero Lebesgue measure, so the endpoint does not change this identity. This proves both convergence and a strictly positive reconstruction constant without classifying translation-invariant operators or invoking distributions.

F1F2F7step 5.1
7.1

Since AK,Nfκf^1, F7 passes its testing inequalities to the limit against any bounded compactly supported g. Under the strong hypothesis this yields TNf,gκ1Bfpgp for every such g. To recover the norm, use the following bounded truncations: put gn,R=1[R,R]1{TNf>1/n}TNfp2TNf for integers n,R1, defined as zero where TNf vanishes. Each gn,R is bounded and compactly supported, so the testing inequality applies to it. Indeed its magnitude is at most TNfp1f^1p1, since p>1, with value zero at zeros of TNf. The same bound also holds without the lower level cutoff; the cutoff is a permissible convenience, not a remedy for a singularity. Writing Jn,R=[R,R]{TNf>1/n}TNfp, the definition gives both TNf,gn,R=Jn,R and gn,Rp=Jn,R1/p, because (p1)p=p. Hence Jn,Rκ1BfpJn,R1/p and Jn,R1/pκ1Bfp. If Jn,R=0 the second inequality holds trivially; otherwise divide. The level sets increase with n and R, so F8 gives TNfpκ1Bfp. Under the weak hypothesis, take g to be the phase of T_Nf on E={TNf>λ}[R,R], a bounded compactly supported function. The limiting testing inequality gives λm(E)pκ1Bfpm(E)11/p and hence the stated weak bound for T_Nf after increasing R, also when m(E)=0. These bounds are uniform in the arbitrary measurable selector.

F1F7F8step 1.2step 5.1step 6.1
8.1

Enumerate the rational cutoffs and apply F1's least-index selector to each finite maximum. Step 7.1 gives the same strong or weak bound for every finite maximum. These maxima increase to CRf because the cutoff is continuous in its parameter. F8 gives the strong norm estimate; continuity from below of their level sets gives the weak estimate, equivalently another use of F8 on their indicators. This proves the Statement. The finite-to-infinite index limits, parameter averages and cutoff limits were justified separately with explicit majorants. AC is inherited through F11; there is no selector for an unattained infinite supremum and no new arbitrary-index choice.

F1F8F11step 7.1
DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-5.6-terra)Open item page →

Density size and tree count for carleson tiles

Definition

Assume The Axiom of Choice as inherited from the packet and real-line analytic conventions. Use the tiles, plus trees, fixed packets and finite model of Carleson tiles wave packets and tile order, and a measurable selector N:RR as in Carleson operator and measurable linearisation. Fix fL2(R), a measurable testing set E of finite measure, and the integer κ=20. Put χI(x)=I1(1+xc(I)/I)κ. For a tile s and a finite tile set S, define densE,N(s)=supt>sE{x:N(x)ωt}χIt(x)dx,densE,N(S)=supsSdensE,N(s), where t>s means st and st in the tile order. The supremum over t ranges over all strict ancestors of s, not just members of S. This family is nonempty: a dyadic spatial parent of Is, paired with either dyadic half of ωs of reciprocal length, is a strict ancestor. The integral tests the whole frequency interval ωt. Every integrand is nonnegative measurable. Substitution u=(xc(I))/I and the antiderivative of (1+u)20 on [0,) give χI=2/19, so densities are finite and between zero and 2/19. For empty S define density zero; for null E every integral is zero. Null modifications of N or E leave each integral, and hence its supremum, unchanged.

Define sizef(S)=sup(T,t)(1ItsTf,ϕs2)1/2, where the supremum ranges over nonempty plus subtrees TS with designated top t. In particular singleton trees with their own tile as top are allowed. Empty S has size zero. This supremum is finite: if =minsSIs>0, each candidate top has length at least ell and the numerator is at most the finite sum over S of the finite squared coefficients. Size zero is equivalent to every coefficient being zero: the forward implication follows from singleton trees and the reverse from the displayed sum. The pairing depends only on the L2 class of f.

A displayed forest is a finite family of disjoint tile subcollections, each equipped with a designated top and forming a tree. Its count is TIT, where IT means its designated top interval. This counts top lengths with multiplicity even when intervals overlap. The empty forest has count zero. A different assignment of tops may change the count; no intrinsic count is attached to a tile set without a forest or an explicit existence assertion about such a decomposition. Restricting S can only decrease density and size, because it restricts the families in their suprema. No selection estimate is part of these definitions.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson density selection

Statement

Assume AC. Let S be a finite tile family, E a finite-measure testing set and N a measurable selector, and let δ=densE,N(S)>0. There is a partition of S into a remainder of density at most δ/2 and finitely many trees with designated tops satisfying TITCδ1m(E). The constant depends only on the fixed exponent κ=20.

Facts & Assumptions

[F1]

Density uses all dominating tiles, the weight with exponent 20, and designated forest tops Density size and tree count for carleson tiles.

[F2]

Assume AC The Axiom of Choice, as in F1's packet conventions.

Proof

Given: The finite family S with positive density delta.

1.1

Write d(t)=E{Nωt}χIt. Consider all tiles t dominating some s in S and satisfying d(t)>δ/2. They form a finite nonempty set: d(t)m(E)/It implies It<2m(E)/δ, while domination implies ItminsSIs>0. Only finitely many dyadic scales lie in this range; at each scale there is one spatial ancestor of each fixed s and finitely many frequency subintervals of its frequency interval. Nonemptiness follows from the definition of positive supremum delta. Let Tops be the maximal members of this finite set. Assign each s lying below a member of Tops to one such top using a fixed finite ordering. This gives disjoint trees. Every s left over has density at most δ/2: otherwise a witness t with d(t)>δ/2 would dominate s and extend to a maximal candidate.

F1F2
2.1

For a top t let I=It, ω=ωt, and let 2kI be the interval with the same center and length 2kI. There is an integer k0 with m(E{Nω}2kI)cδ22kI, where c is an absolute positive constant fixed below. Indeed, partition the line into I and the shells 2kI2k1I for k>=1. On the kth shell χIA220k/I for A=240, and the same bound with k=0 dominates its value on I. If every proposed mass bound failed, then d(t)Acδk0218k. Taking c=(8A)1 makes this less than δ/2, contradicting the choice of t. Assign the least successful k to t, and call its assigned family Tk.

F1step 1.1
3.1

Fix k. Greedily choose from Tk a tile with greatest spatial length, retain it, and remove every remaining tile whose enlarged rectangle 2kIs×ωs intersects its enlarged rectangle. Continue until none remain; this is a finite procedure. Retained enlarged rectangles are disjoint, so their sets E{Nωt}2kIt are disjoint. Step 2.1 gives t retainedIt(cδ22k)1m(E).

step 1.1step 2.1
4.1

A tile s removed by a retained t has IsIt and overlapping frequency intervals; dyadic nesting and reciprocal lengths imply ωtωs. All tiles removed by this same t therefore have mutually overlapping, hence nested, frequency intervals. Since Tops is an antichain, their original spatial intervals must be pairwise disjoint: otherwise dyadic nesting of both coordinates would make two tops comparable. Also overlap of the enlarged spatial intervals implies each original Is lies in the interval centered at c(It) of radius (3/2)2kIt. Thus the sum of their lengths is at most 32kIt. Including t itself in its removed family, and using step 3.1, yields sTkIs(3/c)2kδ1m(E). Summing over k gives tTopsIt(6/c)δ1m(E). Together with step 1.1 this proves the partition and count assertion. Empty assigned trees may be deleted, only reducing the count. AC is inherited; the selections here are finite greedy choices and least integers.

F2step 1.1step 2.1step 3.1
LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson size selection

Statement

Assume AC. For a finite family of size sigma>0, choose trees with total top length <=C sigma^(-2)||f||_2^2, leaving size <=sigma/2.

Facts & Assumptions

[F1]

Size is the supremum of the normalized squared coefficient sums over plus trees, including singleton trees; it decreases on subcollections. Count sums designated top lengths with multiplicity Density size and tree count for carleson tiles.

[F2]

Tiles, plus trees and the fixed packets have the stated dyadic order, Fourier supports strictly inside the lower frequency halves, and Schwartz decay at every integer exponent Carleson tiles wave packets and tile order.

[F3]

Plancherel preserves the complex inner product Plancherel theorem.

[F4]

The complex pairing is first-variable-linear, has squared norm on the diagonal and satisfies Cauchy–Schwarz The complex L2 pairing is well-defined and satisfies Cauchy–Schwarz.

[F5]

Assume AC The Axiom of Choice, sufficient for the countable-choice Fourier interfaces in F2 and F3.

Proof

Given: A finite tile set S, fL2(R), and σ=sizef(S)>0. Put as=f,ϕs. Constants below depend only on the fixed packet.

1.1

Call a plus tree with top t strict when ωtωs,+ for every member s, so its top is not a member. Any plus tree can be made strict by replacing its top t with t~=Itparent×ωt,+. This is a tile, dominates all its members, and satisfies the strict condition even for s=t. Its top length is twice the old length. Consequently, whenever a stock R has size greater than σ/2, it has a strict plus subtree U with top t and sUas2>σ2It/8. Indeed the supremum in F1 then has a witness with normalized sum greater than σ2/4, and the replacement divides that sum by two.

F1F2given
1.2

For l=Isl=Is, packet decay implies ϕs,ϕsCl/l(1+c(Is)c(Is)/l)20. To prove it, use exponent 40 in F2. For each x, the factor 1+c(Is)c(Is)/l is at most (1+xc(Is)/l)(1+xc(Is)/l). Extract its inverse twentieth power from the product of the two decay factors. The remaining integral is at most (1+xc(Is)/l)20dx=(2/19)l. Multiplying by (ll)1/2 proves the bound. By F3, a Gram entry is zero when the two lower frequency halves are disjoint.

F2F3
2.1

Starting with R=S, consider all strict witnesses satisfying the threshold inequality of step 1.1. Only finitely many possible tops occur: if B=sSas2 and 0=minsSIs, their lengths lie between 0 and 8B/σ2. There are finitely many dyadic scales in this range. Each top dominates some s in S, so its spatial interval is the unique ancestor of Is at its scale, and its frequency interval is one of finitely many dyadic subintervals of ωs at its prescribed length. Choose a possible top of minimum frequency center, breaking ties by any fixed ordering of this finite list, and choose a strict witness U for that top. Remove the full tree V={sR:st}, record U and V with that same top, and repeat. The process stops after at most |S| removals because every witness is nonempty. Possible witnesses only disappear as R shrinks, so the selected top-frequency centers are nondecreasing. At termination the residual size is at most σ/2 by step 1.1. The V are disjoint tile collections, as are their subsets U.

F1F2step 1.1
3.1

The strict witnesses have the following separation property. If sU, sU belong to different recorded witnesses and ωs,ωs,, dyadic nesting implies ωsωs,. Thus the top-frequency interval of U lies in ωs,, while that of U' lies in ωs,+. The former center is smaller, so U was selected earlier. If Is met IU, the inequalities Is<IsIU and dyadic nesting would give IsIU. Together with ωUωs this would have removed s' with the earlier full tree. Hence IsIU=. Within one strict plus tree, distinct lower frequency halves are disjoint: if two unequal such halves were nested, the full smaller frequency interval would lie in the larger lower half, whereas the common top frequency must lie in its upper half. Equal lower halves mean equal full frequencies and equal spatial scales; distinct tiles then have disjoint spatial intervals.

F2step 2.1
3.2

Let U be the recorded strict witnesses, L=UUIU, Q=UsUas2, and H=UsUasϕs. The witness threshold and original size give σ2L/8<Qσ2L when L>0. The part of the Gram expansion of H22 with equal lower frequency halves has absolute value at most CQ. In fact, for each fixed frequency the spatial intervals form a subset of the lattice of intervals of its fixed length l. Step 1.2 bounds the row sums and column sums by CjZ(1+j)20<. This series is finite by comparison with the integral of x20 on [1,). Apply 2asasas2+as2 and sum the row and column bounds.

F1F4step 2.1step 1.2
4.1

For fixed sU consider all witness tiles s' whose lower frequency halves properly contain ωs,. They belong to other witnesses by step 3.1, and their spatial intervals lie outside IU. These intervals are pairwise disjoint. To check this, two corresponding lower frequency halves both contain ωs,, so are nested. If unequal, their owning witnesses must differ by step 3.1; applying its separation property to that pair makes the smaller spatial interval disjoint from the other one's entire top interval, hence from the other spatial interval. If equal, they have the same scale and different spatial intervals. Put χI(x)=I1(1+xc(I)/I)20. Since IsIs, the values χIs(x) and χIs(c(Is)) on Is differ by at most a fixed factor: their denominators before taking the twentieth power differ by at most Is/(2Is)1/2. Therefore sIsχIs(c(Is))CRIUχIs(x)dx. All sums here are finite.

F2step 3.1
5.1

Singleton trees in F1 give asσIs. Combining this with step 1.2 bounds an oriented unequal-frequency Gram term by Cσ2IsIsχIs(c(Is)). Thus step 4.1 bounds all such terms by Cσ2UsUIsRIUχIs(x)dx. For a fixed top interval J and a fixed scale l<=|J|, at most one frequency interval can occur above a given spatial interval of that scale: it must be the unique dyadic ancestor of the top frequency of length 1/l. Thus the spatial intervals at that scale form a subset of the dyadic subintervals of J. Write m=J/l and index the full list from k=0 to m-1. Direct integration on the two exterior half-lines bounds lRJχIk(x)dxCl((1+k)19+(1+m1k)19). Indeed the exact two denominators are 1+k+1/2 and 1+mk1/2, with factor l/19. The sum over k is at most Cl by integral comparison with x19. Summing over the dyadic scales l=J2j, j>=0, gives at most C|J| by the geometric sum. The oriented contribution is consequently at most Cσ2L; its conjugate orientation satisfies the same bound.

F1F2step 1.2step 4.1
6.1

Steps 3.2 and 5.1, and the vanishing of every remaining Gram entry in step 1.2, give H22Cσ2L. By the first-variable-linear convention, f,H=Q, so F4 gives Qf2H2. If L>0, combine this with Q>σ2L/8 and divide by σL to obtain LCσ2f22. If no witness was selected, L=0 and the bound is immediate. The removed full trees V have these same tops, so this is their forest count, while step 2.1 gives the required residual size. No assumption that distinct top intervals are disjoint was made. All selections are finite; AC is inherited solely from the Fourier interfaces identified in F5.

F4F5step 2.1step 1.2step 3.2step 5.1
LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson single tree estimate

Statement

Assume AC. For a finite tree T the absolute bilinear tile contribution is <=C density(T) size(T)|I_T|, for |g|<=1_E.

Facts & Assumptions

[F1]

Density uses all tiles above each member, tests their whole frequency intervals with χI=I1(1+xc(I)/I)20, and size bounds every plus subtree and every singleton coefficient Density size and tree count for carleson tiles.

[F2]

The dyadic tile order, plus trees, finite model, normalized Fourier convention and fixed Schwartz packet with transform ψ are as defined Carleson tiles wave packets and tile order.

[F3]

Fourier transformation preserves the complex inner product Plancherel theorem.

[F4]

The centered real-line maximal operator M is bounded on L2 The centered maximal operator is bounded on Lp(Rn) for 1<p<.

[F5]

The complex pairing is sesquilinear and satisfies Cauchy–Schwarz The complex L2 pairing is well-defined and satisfies Cauchy–Schwarz.

[F6]

Schwartz convolution is Schwartz and its transform is the product of the transforms Schwartz convolution and product laws.

[F7]

Fourier inversion holds everywhere for Schwartz functions Fourier inversion on Schwartz space.

[F8]

Nonnegative increasing integrands pass to the limit under the integral Monotone convergence for the integral.

[F9]

Assume AC The Axiom of Choice, supplying the countable choice in the Fourier and Euclidean maximal interfaces.

Proof

Given: A finite tree T with designated top t, measurable selector N, fL2, and measurable g with g1E, m(E)<. Put J0=It, L=J0, δ=densE,N(T), σ=sizef(T) and as=f,ϕs. All constants depend only on the fixed packet.

1.1

The proof establishes the stronger sum of absolute tile contributions. Choose complex numbers εs of modulus one so that εsas1ωs,+(N)ϕs,g is nonnegative real, taking εs=1 for a zero product. Finite sesquilinearity bounds that sum by Esεsas1ωs,+(N(x))ϕs(x)dx. Write bs=εsas. Empty T and sigma=0 give zero. If delta=0, exponent20 packet decay bounds the integral of ϕs on EN1(ωs,+) by CIsδ=0 for each s, so all contributions vanish. Hence assume delta,sigma>0. Singletons give bsσIs. A possible member s=t contributes at most CσLEN1(ωt)χJ0CσδL, by packet decay with exponent20. Remove this one member. Each remaining tile has its top frequency contained in exactly one half of its frequency interval. Split them into the strict plus and strict minus trees according as that half is right or left, retaining the same top t and the bounds delta,sigma.

F1F2F5given
1.2

We record a kernel bound. If Kr satisfies Kr(z)Cr1(1+z/r)20 and h is Schwartz, then for every interval J of length j<=r and every x,y in J, (Krh)(x)CMh(y). Indeed xyr makes the displayed weight at x comparable to that at y. On zy<r its integral against |h| is bounded by twice the centered average times a constant. On the annulus 2nrzy<2n+1r, it is at most C219nMh(y), using the centered interval of radius 2n+1r. The geometric sum proves the bound uniformly in x,y,r.

F4
2.1

For either nonempty strict subtree U, partition the line into maximal dyadic intervals J such that 3J contains no Is, s in U; 3J denotes the concentric interval of triple length. Such intervals cover the line: all sufficiently small dyadic intervals have this property because U is finite with positive minimum spatial length, and sufficiently large ancestors of any fixed interval fail it because their triples eventually contain every fixed bounded interval. The property passes to dyadic subintervals since their triples lie in the larger triple; maximal good intervals are therefore disjoint and cover every point. They form a countable family J. If j=|J| and Jp is its dyadic parent, maximality supplies an sU with Is3Jp. Since this latter interval has length6j, dyadic lengths imply Is4j. In particular dist(J,J0)6j. If j>=L and J meets J0, or if j>=L+dist(J,J_0) when they are disjoint, then J03J, contradicting goodness. Consequently intervals with dist(J,J_0)<L have j<2L and lie in a fixed concentric enlargement of J0; their total length is at most CL. When d=dist(J,J_0)>=L, one has d/6j<d+L2d.

F2step 1.1
2.2

For the strict plus subtree put H=sUbsϕs, a Schwartz function. Then H2CσL. Here are the needed orthogonality details. Lower frequency halves at unequal scales are disjoint: if nested, the full smaller frequency interval lies in the larger lower half, whereas the common top lies in its upper half. Plancherel kills those Gram entries. At equal frequency and spatial scale l, packet decay gives ϕs,ϕsC(1+c(Is)c(Is)/l)20: extract the center-distance factor from the product of exponent40 decay bounds by the triangle inequality, and integrate the remaining normalized exponent20 weight, whose integral is2/19. The row and column sums over the length-l spatial lattice are bounded by CnZ(1+n)20. Using 2bsbsbs2+bs2 proves H22Csbs2Cσ2L, the last bound being the defining plus-tree size inequality.

F1F2F3F5step 1.1
3.1

On each J split the signed tile sum into small tiles Is4j and large tiles Is8j. These are all the possibilities because lengths are dyadic. The integral of the absolute small sum is at most CσδsU:Is4jIs(1+dist(Is,J)/Is)20. To see this for each tile of length l, exponent40 packet decay and the singleton bound give bsϕs(x)CσlχIs(x)(1+xc(Is)/l)20. Take the supremum of the last factor on J and integrate over EN1(ωs,+), a subset of the full-frequency set controlled by delta. At a fixed scale l, there is at most one possible frequency for each spatial interval, the unique ancestor of the top frequency of length1/l. The spatial intervals thus form a subset of the length-l dyadic lattice inside J0. If l<=j, goodness and aligned dyadic endpoints put these intervals outside 3J, at distance at least j from J. Summing the displayed weights over that lattice gives at most Clnj/l(1+n)20Cj(l/j)20 by integral comparison. For l=2j or4j, the full lattice sum is at most Cl<=Cj, with no separation needed. Summing dyadic l<=4j bounds the displayed spatial sum by Cj.

F1F2step 1.1step 2.1
3.2

Let GJ=EJsU:Is8jN1(ωs,+), the support of the large sum on E. Then m(GJ)Cδj. If there is no large tile the claim is immediate. Otherwise 8j<=L. For the witness s' in step 2.1, let v have spatial interval the dyadic ancestor of Is of length4j and frequency the dyadic ancestor of the top frequency of length1/(4j). Then v is a tile above s', so its weighted density integral is at most delta. The center of its spatial interval is within Cj of J, because Is3Jp and its ancestor has length4j. Hence χIvc/j on J for an absolute c>0. Every large tile frequency is contained in ωv, since its length is at most1/(8j) and it also contains the top frequency. This gives GJEJN1(ωv) and proves the measure bound. Moreover 8j<=L and the witness geometry in step 2.1 place all such J in one fixed enlargement of J0. Thus their total length is at most CL and Jm(GJ)CδL. The use of 4j for the density witness is essential: the witness in 3 times the parent need not have length at most j.

F1F2step 2.1
3.3

We construct exact smooth scale projections for this plus subtree. For each dyadic r<=L, let Ωr=[αr,αr+1/r) be the unique dyadic ancestor of the top frequency of length1/r. Set Θ(v)=ψ((v1/2)/5). The exact plateau a=1/9 and support b=1/8 in F2 give Θ=1 on [0,1] and Θ=0 outside [-1/8,9/8]. Its inverse Fourier transform is k(x)=5eπixϕ(5x), as follows by substituting v=5u+1/2 in the absolutely convergent inverse integral. Thus the inverse transform of Θ(r(ξαr)) is Kr(x)=r1e2πiαrxk(x/r), bounded by Cr1(1+x/r)20. For any member with Isr, its Fourier support lies in Ωr, so this multiplier is one on its support. If Is<r, its full frequency is a proper ancestor of Ωr and its lower packet support is separated from its upper half by at least 1/(8Is)1/(4r). Since the top, hence Ωr, lies in that upper half, the enlargement of Ωr by1/(8r) on each side misses that packet support. The multiplier is therefore zero there. By F6 and F7, KrH=sU:Isrbsϕs everywhere, not merely as an unspecified truncation estimate.

F2F6F7step 2.2
4.1

The small terms sum to at most CσδL over all J. For dist(J,J_0)<L this follows from the Cj bound and the total-length bound in step 2.1. For d=dist(J,J_0)>=L, each scale l<=L has total spatial length at most L, so its contribution to the spatial sum of step 3.1 is at most L(1+d/l)20. Summing the dyadic scales gives CL(L/d)20. For 2kLd<2k+1L, step 2.1 gives j2kL/6 and j<2^{k+2}L. The disjoint J in this shell lie in an interval of length C2kL, so their number is bounded by an absolute constant. Summing CL220k for k>=0 completes the estimate. Countable sums of these nonnegative integrals are justified by F8 applied to finite unions of the disjoint J.

F8step 2.1step 3.1
4.2

For each x, the upper frequency halves of a strict plus tree form a nested family, with all tiles at a fixed spatial scale having the same frequency. If N(x) belongs to an upper half, it belongs to all the larger upper halves in this family. Thus the activated spatial scales form an initial segment of the scales present in U. Let tau(x) be their largest spatial length, setting it to zero when none is active. Every strict member has length at most L/2, so 2tau(x)<=L whenever tau(x)>0. On J, if tau(x)>=8j, the large sum equals the scale block 8jIsτ(x), which by step 3.3 is ((K8jK2τ(x))H)(x). If tau(x)<8j the large sum is zero. Both kernel scales are at least j. Step 1.2 therefore bounds the magnitude of the large sum, for x in E intersect J, by C1GJ(x)infyJMH(y). Denote that infimum by q_J; it is an ordinary fixed nonnegative number, not a measurability assertion about uncountably selected points. By F4 and step 3.2, Jm(GJ)qJ2CδJjqJ2CδR(MH)2CδH22. The middle inequality holds because MH(y)qJ on each disjoint J. Countable sums are again obtained from F8; alternatively the inequalities first hold for any finite subfamily.

F2F4F8step 1.2step 3.2step 3.3
5.1

For the strict minus subtree, upper frequency halves at unequal scales are disjoint. If two were nested, the full smaller interval would lie in the larger upper half, but the top frequency is required to lie in its lower half. At a fixed x, the selector N(x) therefore activates at most one spatial scale. At that scale, bsϕs(x)Cσ(1+xc(Is)/Is)20 and the sum over the spatial lattice is at most Cσ, uniformly in x. The large sum on J consequently has magnitude at most Cσ1GJ on E. Its integrals sum to CσδL by step 3.2. Together with step 4.1 this proves the bound for the minus subtree.

F2step 1.1step 4.1step 3.2
6.1

Cauchy–Schwarz on the disjoint union of the GJ, first for finite subfamilies and then by F8, bounds the total integral of the large plus sum by C(Jm(GJ))1/2(Jm(GJ)qJ2)1/2CδLH2CδσL. Combine this with the small contribution from step 4.1, the minus contribution from step 5.1 and the possible top contribution from step 1.1. This proves the stronger absolute sum and hence the stated absolute bilinear bound. All selector and testing-set boundary conventions are half-open and were respected in the frequency inclusions; null modifications leave the integrals unchanged. AC is inherited through F9; the only new choices were finite phase choices and explicitly defined dyadic ancestors. The maximal estimate is used solely on Lebesgue measure on the line, with its valid countable-choice and sigma-finite specialization.

F5F8F9step 1.1step 4.1step 3.2step 5.1step 2.2step 4.2
LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson forest summation gives restricted weak ltwo

Statement

Assume AC. For every finite linearised tile family, CS,Nf,gCf2m(E)1/2 for g1E, where E is measurable of finite measure. Constants do not depend on S or N.

Facts & Assumptions

[F1]

A finite positive-density family can be partitioned into trees of total top length at most Cδ1m(E) and a remainder of density at most δ/2 Carleson density selection.

[F2]

From a finite family of positive size σ, one can choose finitely many trees of total top length at most Cσ2f22 such that deleting their union leaves a remainder of size at most σ/2 Carleson size selection.

[F3]

A finite tree's absolute bilinear contribution is at most its density times its size times CIT Carleson single tree estimate.

[F4]

Density is at most D=2/19, size is finite and vanishes exactly when all packet coefficients vanish, and both decrease on subcollections. Forest count sums designated top lengths with multiplicity Density size and tree count for carleson tiles.

[F5]

Assume AC The Axiom of Choice, inherited from the three selection/estimate suppliers.

Proof

Given: A finite S, measurable N, fL2, and g1E with e=m(E)<.

1.1

If S is empty or its size is zero, all summands vanish by F4. If f2=0, all coefficients are zero. If e=0, g is zero almost everywhere and the testing integrals vanish. Hence assume F=f2>0, e>0 and positive initial size. Put A=F/e, sn=A2n and dn=Dmin(1,4n) for integers n.

F4given
2.1

Choose an integer n00 with sn0sizef(S), possible because the size is finite and 2n as n. Set Rn0=S. We construct decreasing finite remainders Rn with size at most sn and density at most dn. The initial density bound holds since dn0=D. At step n, the tiles removed from Rn will be equipped with a forest Fn of total top length at most C0e4n, for one constant independent of n and S.

F4step 1.1
3.1

First reduce the density of Rn to dn+1. If its density is already at most that threshold, do nothing. Otherwise apply F1 using its actual density delta. Each such application removes trees of count at most Ce/δCe/dn+1 and halves the remaining density. For n<0 no application is needed, since dn=dn+1=D. For n>=0, dn=4dn+1, so at most two applications suffice, even when the first remaining density is zero. The removed trees all lie in Rn. For n>=0 their combined count is at most 2Ce/dn+1=8CD1e4n.

F1F4step 2.1
4.1

The remainder after step 3.1 still has size at most sn. If its actual size sigma exceeds sn+1, apply F2 once. Its output remainder has size at most σ/2sn+1 and its selected trees have total top length at most CF2/σ2CF2/sn+12=4Ce4n. Order those finitely many trees and replace the ith one by the tiles in it which occur in none of the earlier trees, retaining its designated top and discarding it if empty. Every resulting nonempty collection is still a tree, their union and hence the output remainder are unchanged, and their total top length cannot increase. Thus they form a forest in the required partition sense. Otherwise remove nothing. Density cannot increase in this step. Call the resulting remainder Rn+1 and combine this forest with all forests removed by the density selections into Fn. Tile collections coming from different selections are disjoint because each selection operates on what remains. Their designated tops may overlap; the count bounds already include this multiplicity. This proves the induction and the promised bound with a fixed C0.

F2F4step 1.1step 2.1step 3.1
5.1

Only finitely many nonzero coefficient tiles can survive this process. More explicitly, for each such s in the original finite S the positive number f,ϕs/Is is a lower bound for the size of any remainder containing s, by the singleton-tree case of F4. The minimum b of these finitely many positive numbers is positive. Choose n1>n0 with sn1<b. Then Rn1 contains only zero-coefficient tiles and has zero testing contribution. Thus the finite forests Fn for n0n<n1 account for the whole testing form; no infinite decomposition, limiting selector or interchange of integrals is required. If no coefficient was nonzero, step 1.1 already handled the case.

F4step 1.1step 4.1
5.2

Every tree in Fn is a subcollection of Rn, so its size and density are at most sn,dn. By F3, the triangle inequality over the finitely many trees, and the count bound, the magnitude of their combined testing contribution is at most CsndnTFnITCC0DFemin(2n,2n). The equality of the powers follows from 2n4nmin(1,4n)=min(2n,2n). This uses the tree estimate on each actual designated tree, rather than assuming the forest's tops are spatially disjoint.

F3F4step 1.1step 4.1
6.1

Sum step 5.2 over n0n<n1. The sum is at most the two convergent geometric tails n<02n+n02n=1+2=3; finite geometric identities give the same uniform upper bound without invoking an infinite exchange. Step 5.1 then proves the displayed inequality after absorbing 3CC0D into the constant. All choices concern finitely many stopping stages for this S. The AC assumption is inherited from the three analytic suppliers, not a new unrestricted choice of infinite forests.

F5step 5.1step 5.2
Scratch work

The joint stopping and summation proof and both formerly incomplete suppliers now have full local authored arguments. The size-selection and single-tree repairs await ordinary mathematical review and root decision reconciliation; this file does not itself claim those reviews or a source disposition. Other Carleson maximal-theorem prerequisites remain separate holds.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson signed tree weak one one estimate

Statement

Assume AC. Let T be a finite plus tree with designated top t, J0=It, and L=J0. Put wJ0(y)=(1+yc(J0)/L)20. For every measurable complex f with fwJ0<, all coefficients f,ϕs converge absolutely. For every choice of complex εs with εs1, define the signed packet projection PT,εf=sTεsf,ϕsϕs. There is a constant C depending only on the fixed packet, independent of T, its top, the coefficients epsilon, f and lambda, such that for every λ>0, m{x:PT,εf(x)>λ}CλRf(y)wJ0(y)dy. In the normalized bump notation of Lacey (3.4), specialized to the exponent twenty used here, χJ0(y)=L1(1+yc(J0)/L)20=L1wJ0(y). Thus the right-hand side is exactly the fLχJ01 localization in Lacey (7.11), and the estimate includes arbitrary signs.

Facts & Assumptions

[F1]

The dyadic packet construction gives Schwartz packets, their exact modulation/dilation formula, Fourier support in the lower halves, and the plus-tree order Carleson tiles wave packets and tile order.

[F2]

Plancherel preserves the complex inner product Plancherel theorem.

[F3]

The complex pairing is sesquilinear and obeys Cauchy–Schwarz The complex L2 pairing is well-defined and satisfies Cauchy–Schwarz.

[F4]

A locally integrable complex function has vanishing averaged absolute oscillation at almost every point Almost every point is a Lebesgue point of a locally integrable function.

[F5]

Nonnegative increasing integrands pass to the limit under the integral Monotone convergence for the integral.

[F6]

Assume AC The Axiom of Choice, supplying the countable choice in the Fourier and differentiation interfaces.

Proof

Given: T,t,J0,L and coefficients epsilon as in the Statement. All constants below are uniform in these data and depend only on the fixed Schwartz packet.

1.1

Empty T gives the zero operator. Otherwise put ξ0=c(ωt) and qs(x)=e2πiξ0xϕs(x). Because ξ0ωs, direct differentiation of the explicit packet formula and the Schwartz bounds for phi and its derivative give, for every integer M>=0 and l=|I_s|, qs(x)CMl1/2(1+xc(Is)/l)M,qs(x)CMl3/2(1+xc(Is)/l)M. Indeed the remaining modulation frequency c(ωs,)ξ0 has absolute value at most1/l. The conjugated operator Qh=sεsh,qsqs satisfies Pf=e2πiξ0xQ(e2πiξ0f), so modulation changes none of the input norms or output level sets.

F1F3given
1.2

We prove the unweighted bound m{Qh>λ}Ch1/λ for every complex h in L1. Choose the maximal dyadic intervals J with J1Jh>λ. Their lengths are bounded above by h1/λ, so each such interval lies in a maximal one. The selected intervals are disjoint and countable, and JJh1/λ. Maximality of J and its parent give Jh2λJ. Outside their union, every dyadic average at the point is at most lambda. At a Lebesgue point of h, its average oscillation on the shrinking dyadic intervals is bounded by twice the average oscillation on the centered interval of radius equal to their length, hence tends to zero by F4. Thus hλ almost everywhere outside the union. If no such interval exists the same conclusion applies on the whole line.

F4F5given
2.1

The operator Q has a uniform L2 bound. First omit a possible member t. The remaining plus tree is strict. Its lower frequency halves at unequal scales are disjoint: nesting would put the full smaller frequency interval in the larger lower half, while the common top frequency must lie in the upper half. At equal frequency and scale l, the Gram entries satisfy qs,qsC(1+c(Is)c(Is)/l)20. To verify this, use exponent40 in step 1.1, extract the inverse twentieth power of the center distance using 1+cc/l(1+xc/l)(1+xc/l), and integrate the remaining normalized weight, with integral2/19. The spatial intervals of a fixed scale form a subset of a dyadic lattice, so the row and column sums are bounded by CnZ(1+n)20<. Plancherel kills the other entries. Expanding a finite synthesis sum and applying 2zszszs2+zs2 gives zsqs22Czs2. The possible top adds a single term of fixed norm; u+v222u22+2v22 preserves a uniform synthesis bound. For as=h,qs and A=(as2)1/2>0, pair h with (as/A)qs and use F3 to get ACh2. If A=0 this is immediate. The synthesis bound applied to εsas therefore proves Qh2Ch2.

F1F2F3step 1.1
2.2

The finite smooth kernel K(x,y)=sεsqs(x)qs(y) satisfies K(x,y)Cxy1 and yK(x,y)Cxy2 for x unequal to y. At each scale l there is only one possible frequency interval, the unique ancestor of the top frequency of length1/l. The spatial centers are a subset of the length-l lattice. Put d=|x-y|. Using exponent6 in step 1.1, extract (1+d/l)3 from the product of the two decay weights and sum the remaining weight over this lattice. The latter sum is bounded uniformly, by comparison with nZ(1+n)3. The scale-l kernel and derivative sums are thus bounded by Cl1(1+d/l)3 and Cl2(1+d/l)3. For l<=d, the derivative bound is at most Cl/d3, whose dyadic sum is at most C/d2; for l>d sum Cl2. The kernel estimate follows identically, summing Cl2/d3 below d and Cl1 above d. All actual scale sets are finite subsets of these geometric sums.

F1step 1.1
3.1

Set hJ=J1Jh, bJ=(hhJ)1J, and let g equal hJ on J and h elsewhere. Then bJ=0, bJ12Jh, g1h1 and g2λ almost everywhere. Hence g222λh1. By step 2.1 and the elementary inequality u21u>aa21u>a, m{Qg>λ/2}Ch1/λ. The union B of the concentric triple intervals 3J has measure at most 3h1/λ. Outside 3J, cancellation gives QbJ(x)=J(K(x,y)K(x,c(J)))bJ(y)dy. For y in J and x outside 3J, every point between y and c(J) is at distance at least (2/3)xc(J) from x. Integrating the derivative bound in step 2.2 along that segment gives K(x,y)K(x,c(J))CJ/xc(J)2. Its x-integral outside 3J is at most an absolute constant. Consequently R3JQbJCbJ1.

F3step 2.1step 2.2step 1.2
4.1

The sum b=JbJ converges in L1, since JbJ12h1. Each packet is bounded and the operator sum has finitely many packets, so convergence in L1 implies coefficient convergence and pointwise convergence of Q applied to partial sums. The triangle inequality and F5 therefore give RBQbCh1. Markov's inequality, here the direct bound uam{u>a} for nonnegative u, bounds the measure where Qb>λ/2 outside B by Ch1/λ. Since h=g+b, step 3.1 and the measure bound on B prove the desired unweighted weak estimate. The same bound holds for P by the modulation identity.

F5step 1.1step 3.1
5.1

Now assume only the weighted integrability in the Statement and split f=f0+f on 3J0 and its complement. On 3J0 the weight is at least (5/2)20, so f01CfwJ0 and step 4.1 applies. For y outside 3J0, put d=dist(y,J0)>=L. Exponent40 packet decay gives sTϕs1ϕs(y)ClL, l dyadic(L/l)(1+d/l)40C(L/d)40CwJ0(y). Here ϕs1=lϕ1 and there are at most L/l spatial intervals at scale l. The middle bound is the geometric sum of (l/L)39 after extracting (L/d)40; the last uses yc(J0)=d+L/2. The same estimates show ϕs(y)CswJ0(y) on the line for each fixed s, with a finite constant Cs: on 3J0 both the positive lower bound for w and boundedness of the packet suffice, and outside it the displayed estimate applies. Thus all coefficient integrals are absolute, as claimed. Finite summation and the displayed bound yield Pf1CfwJ0.

F1step 4.1
6.1

Use the unweighted weak estimate for Pf0 at threshold lambda/2 and the L1 bound for Pf with Markov's inequality at the same threshold. Since PfPf0+Pf, their two exceptional sets cover the desired one and give the stated weighted weak estimate. The proof covers a singleton top, empty tree, zero input class and all zero coefficients epsilon; lambda is required positive only for the displayed divisions. All decompositions are explicitly dyadic and countable; AC is inherited through F6 and is not used for a new arbitrary-index selection.

F6step 4.1step 5.1
LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Hunt exceptional set and distribution estimates

Statement

Assume AC. Let E,F be measurable subsets of the line with 0<e=m(E)< and 0<h=m(F)<. There is a measurable FF with m(F)h/2, depending only on E,F, such that for every finite tile family S, measurable selector N, and measurable complex inputs f1E, g1F, CS,Nf,gCmin(e,h)(1+log(e/h)). The constant is independent of all these data. Consequently, for every 1<q< there is Cq independent of S,N,E such that m{CS,N1E>λ}Cqqλqm(E)(λ>0). Thus the finite models are uniformly restricted weak type (q,q) at every such exponent.

Facts & Assumptions

[F1]

The exact fixed packets have nonnegative even transform psi, plateau a=1/9, support b=1/8, the dyadic plus-tree order and the finite testing form Carleson tiles wave packets and tile order.

[F2]

Density is at most D=2/19, size is the supremum over all plus subtrees, singletons are allowed, and forest count sums designated top lengths Density size and tree count for carleson tiles.

[F3]

Every signed plus-tree packet projection has weak (1,1) bound with input weight (1+xc(IT)/IT)20, uniformly in signs Carleson signed tree weak one one estimate.

[F4]

Density selection halves positive density at forest count cost at most Cδ1m(F) Carleson density selection.

[F5]

Size selection halves positive size at forest count cost at most Cσ2f22 Carleson size selection.

[F6]

A tree's absolute testing contribution is at most its density times its size times CIT Carleson single tree estimate.

[F7]

The centered maximal operator on the line satisfies m{Mu>t}5u1/t The centered Hardy-Littlewood maximal operator is weak type (1,1).

[F8]
[F9]

Cauchy–Schwarz holds for complex functions, in particular on finite probability spaces The complex L2 pairing is well-defined and satisfies Cauchy–Schwarz.

[F10]

Nonnegative increasing integrands pass to the integral limit Monotone convergence for the integral.

[F11]

Assume AC The Axiom of Choice, supplying the countable-choice Fourier and maximal interfaces.

Proof

Given: E,F,e,h,S,N,f as in the Statement. For an interval J put wJ(x)=(1+xc(J)/J)20 and χJ=J1wJ. Write as=f,ϕs. All constants below depend only on the fixed packet until an exponent q is specified.

1.1

Two elementary weight facts will be used. First, for any interval J and z in J, EχJCM1E(z). The weight centered at c(J) is comparable to the same weight centered at z, and on the centered annuli of radii 2nJ its integral is bounded by C219nM1E(z). Sum the geometric series, also including the inner interval. Second, the packet is bounded below on its spatial interval: for |u|<=1/2, the explicit inverse transform of the even nonnegative psi is ϕ(u)=ψ(ξ)cos(2πuξ)dξ(2/9)cos(π/8)=c0>0. Therefore ϕs(x)c0Is1/2 on Is. Both statements use the exact local plateau and support, not an unspecified wavelet lower bound.

F1F7given
1.2

We record a capped forest sum for use on the remaining family R. If every coefficient of R is zero its form vanishes, so assume at least one is nonzero. Suppose its size is at most B>0, f22e, and the testing set has measure at most h. Let A=max(B,e/h), sn=min(B,A2n) and dn=D4n for n>=0. Starting with R of size<=s_0=B and density<=d_0=D, first reduce density at step n to dn+1=dn/4 by at most two applications of F4 with the actual current density, applying it only above that target. Each application has count at most Ch/dn+1. Then, if the remaining actual size exceeds sn+1, apply F5 once. Because sn+1sn/2, this reaches the next size target. Such an application cannot occur while sn+1=B. When it does occur, sn+1=A2(n+1) and its count is at most Ce/sn+12Ch4n. The density removals also have total count at most Ch4n. Every removed tree lies in the current remainder and therefore has size<=s_n and density<=d_n. The finite singleton lower bounds show termination on all nonzero coefficients: the minimum of as/Is over the finitely many positive coefficients is positive, whereas s_n tends to zero. Thus finitely many recorded forests account for the entire form.

F2F4F5given
2.1

For any plus subtree U with top J, define ΔU(x)2=sUas2Is11Is(x). We claim m{ΔU>λ}Cλ1EwJ(λ>0). Average over the finite probability space of all independent signs εs=±1 and set Z(x)=sεsasϕs(x). At fixed x, B(x)=EZ(x)2=sasϕs(x)2, and expansion of the fourth power gives EZ(x)43B(x)2: only indices occurring an even number of times survive, and the three pairings each contribute at most B squared. Cauchy–Schwarz gives BB/2+(3B2P{Z2>B/2})1/2, hence this probability is at least1/12 when B>0. By step 1.1, Bc02ΔU2. Thus on {ΔU>λ} a fraction at least1/12 of the signs have Z>c0λ/2. Integrate this finite average and apply F3 to each signed projection. Since f1E, the claimed bound follows. No infinite random family or probabilistic limit is used.

F3F9step 1.1
3.1

Here is an explicit subtree bootstrap. Suppose a collection R of tiles has, for every plus subtree U with every designated top J, the bound m{ΔU>λ}C0αJ/λ, with alpha>0. Then sizef(R)Cα. Fix U,J. At each spatial scale there is only one possible frequency ancestor of the top, so each spatial interval I labels at most one tile in U. Put bI=as2/I for it, and put b_I=0 for the other dyadic subintervals. For b_I>0, the singleton bound at threshold bI/2 gives bI4C02α2=B0. Subtrees with spatial intervals inside any dyadic Q contained in J are again plus trees: use Q as spatial top and the ancestor of the old top frequency of length1/|Q|. A member with interval Q may be the new top itself, which is allowed.

F1F2step 2.1
4.1

Write V(x)=ΔU(x)2=IJbI1I(x). There are only finitely many nonzero b_I. For t>=0, descend the finite dyadic subdivision of J down to its smallest active scale and stop at the first interval Q on each branch where the sum of ancestor coefficients, including b_Q, exceeds t. These Q are disjoint and cover {V>t}. The stopped ancestor sum is at most t+B_0. On Q the remaining terms come from the strict descendants of Q, hence form a plus subtree to which the assumed weak square-function bound applies. Put u=B0+4C02α2=8C02α2. The subset of Q where V>t+u has measure at most C0αQ/uB0=Q/2. Summing Q gives m{V>t+u}12m{V>t}. Since V is supported on J, induction gives m{V>nu}2nJ for n>=0. F8 therefore yields Vun0m{V>nu}2uJ. But V=sUas2, so taking the supremum over U,J proves the size bound. Empty descendant subtrees contribute zero. This supplies the distribution bootstrap rather than assuming a John–Nirenberg theorem.

F2F8step 3.1
5.1

Since EwJwJ=(2/19)J, steps 2.1 through 4.1 show that every finite collection with f1E has size at most an absolute constant B_* . If h<=e, take F=F and use this size bound. If h>e, set α=20e/h, Ω={M1E>α} and F=FΩ. By F7, m(Ω)h/4 and hence m(F)3h/4h/2. Let Sout={sS:Is⊈Ω} and Sin=SSout. Every nonempty plus subtree of Sout has a top J containing some member interval that meets Ωc. At such a point z in J, step 1.1 gives EwJCαJ. Steps 2.1 through 4.1 now imply sizef(Sout)CαCe/h. These conclusions hold for every f bounded by the same indicator, and the chosen major subset depends only on E,F.

F7step 1.1step 2.1step 3.1step 4.1
6.1

We bound the inside tiles in the case h>e. For each spatial interval I occurring in Sin choose the greatest integer k>=0 such that the concentric dilation 2kI is contained in Omega. It exists because I is contained and Omega has finite measure; its next dilation contains some point outside Omega. Weight comparison gives χI2k+1χ2k+1I, so step 1.1 at such a point bounds EχIC2kα. Exponent20 packet decay then gives asCI2kα for every tile with this spatial interval. For fixed I, the upper frequency halves of all possible tiles are disjoint, so at each x at most one selector indicator is nonzero. Since FΩc(2kI)c, summing the absolute testing contributions of all these tiles gives at most Cα2kI(2kI)cχICα218kI. The tail integral is at most C219k by direct integration of the weight; the estimate holds also for k=0 with the same absolute constant.

F1step 1.1step 5.1
7.1

At each depth k, the sum of the lengths of the spatial intervals in step 6.1 is at most C(k+1)m(Ω). To prove this, place each I in a maximal dyadic interval Q contained in Omega. Such a Q exists because its possible lengths are bounded by m(Ω); distinct maximal Q are disjoint. Since 2k+1I is not contained in Omega, it is not contained in Q, so I lies within C2kI of an endpoint of Q. At scale I=2jQ, the number of such dyadic subintervals is at most Cmin(2j,2k). Their total length, summed over j>=0, is bounded by CQj0min(1,2kj)C(k+1)Q. Sum over the disjoint Q to obtain the asserted packing. Combining this with step 6.1 gives inside contribution at most Cαm(Ω)k0(k+1)218kCe, since alpha=20e/h and m(Ω)h/4. The series converges, for example by summing its finite geometric derivative. Only finitely many original intervals occur; allowing all intervals in the packing upper bound does not change the estimate.

step 5.1step 6.1
8.1

F6 bounds the contribution of the forest removed at level n of step 1.2 by Chsn, because its count is at most Ch4n and its density is at most D4^(-n). Summing gives CR,Nf,gChB(1+log+(e/h/B)), where log+t=max(0,logt). Indeed the sum of min(B,A2n) has at most 1+log2(A/B) terms equal to B, followed by a geometric tail at most2B; also log(A/B)=log+(e/h/B). Empty R or zero size gives zero without using a positive B threshold. For h<=e apply this bound to R=S with B=B_* from step 5.1. It is at most Ch(1+log(e/h)), after absorbing fixed B_* into C. For h>e apply it to R=Sout with B=Ce/h, for the absolute constant supplied by step 5.1. It is at most Ce(1+log(h/e)). Adding the inside contribution from step 7.1 proves the claimed major-subset estimate for arbitrary g1F.

F6step 5.1step 1.2step 7.1
9.1

Fix 1<q<infinity. For positive e,h, min(e,h)(1+log(e/h))Cqe1/qh11/q. For e>=h use logt(t1/q1)/(1/q) with t=e/h; for h>=e use the same inequality with exponent1-1/q and t=h/e. The elementary inequality follows by integrating u1uθ1 from1 to t for theta>0. Given lambda>0 and u=CS,N1E, take F={u>λ}[R,R] when this has positive measure h. Apply the major-subset estimate and choose g=1Fu/u there, extended by zero. Then u,g=Fuλh/2, so h(Cq/λ)qe. A zero-measure F already satisfies this. Increasing R and using F10 gives the required distribution bound on the whole line. A null E gives zero coefficients directly. The construction respects arbitrary finite families and measurable selectors, so the constant is uniform. AC is inherited through F11, and every random average used in this proof was finite.

F1F10F11step 8.1
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Carleson restricted weak interpolation

Statement

Assume AC. If the uniformly linearised finite model operators are restricted weak type (r,r) and (s,s), with 1<r<p<s<infinity, they are strong type (p,p), uniformly in the selector and finite family.

Facts & Assumptions

[F1]

The layer-cake identity computes hq from the level-set measures, also when the integral is infinite For 0 < p < infinity, the layer-cake formula computes the integral of |f|^p from the distribution function.

[F2]

Weak and strong type have their distribution and norm meanings Sublinear operators and weak or strong type (p,q) bounds.

[F3]

Each finite model is complex-linear and consists of finitely many Schwartz packet coefficients multiplied by measurable selector indicators Carleson tiles wave packets and tile order.

[F4]

Complex Hölder and Minkowski hold, including on finite counting measure spaces Complex Holder, Minkowski, and the quotient norm.

[F5]

Dominated convergence holds Dominated convergence.

[F6]

Assume AC The Axiom of Choice, as inherited by the finite packet construction and its Fourier interfaces.

Proof

Given: 1<r<p<s< and a finite model A. Restricted weak type means that there are constants Kr,Ks independent of A such that for q=r,s, every measurable E with finite measure and every lambda>0 satisfies m{A1E>λ}Kqqλqm(E). This interpretation uses only characteristic inputs; the stronger convention allowing bounded supported inputs also suffices.

1.1

For q>1 define the testing functional Nq(h)=supBm(B)1+1/qBh, where the supremum is over measurable B of finite positive measure. It is homogeneous and subadditive by the integral triangle inequality. If m{h>t}(K/t)q, F1 with exponent one, applied on B, gives Bh0min(m(B),(K/t)q)dt=qq1Km(B)11/q. For K>0 split the integral at t=Km(B)1/q; for K=0 every positive level set is null and the bound is zero. Thus Nq(h)qK, where q=q/(q1). Conversely, if Nq(h)=D<, apply the definition to B={h>t}[R,R]. When its measure is positive, tm(B)1/qD; when zero the same measure bound holds. Increasing R gives m{h>t}(D/t)q. This proves both comparisons even if the original level set could have infinite measure.

F1F2given
2.1

If u is a nonnegative simple function with 0ua1E, list its finitely many positive values in increasing order. It is the sum of their successive nonnegative differences times the indicators of the corresponding superlevel sets. Each such set lies in E and the differences sum to at most a. Linearity and step 1.1 therefore give Nq(Au)qKqam(E)1/q for q=r,s. A complex simple u with ua1E is the sum of the positive and negative real parts and i times the positive and negative imaginary parts, each bounded by a1E. Hence Nq(Au)4qKqam(E)1/q. The same result is zero for null E because its indicator has zero output almost everywhere by the assumed restricted bound and linearity of the finite coefficient formula.

F3step 1.1given
3.1

Let f be a complex simple function supported on a set of finite measure. Put Ek={2k<f2k+1}, mk=m(Ek) and fk=f1Ek. There are only finitely many nonempty E_k, they are disjoint, and f=kfk almost everywhere. For each integer n, split f=fnhigh+fnlow by summing over k>=n and k<n respectively. Subadditivity of the testing functional and step 2.1 give Nr(Afnhigh)Crkn2kmk1/r and Ns(Afnlow)Csk<n2kmk1/s, with Cq=8qKq. Since Af>2n implies that one of the two pieces has magnitude greater than 2n1, the converse comparison in step 1.1 bounds its level-set measure by m{Af>2n}(2Cr)r2nr(kn2kmk1/r)r+(2Cs)s2ns(k<n2kmk1/s)s.

F3step 1.1step 2.1
4.1

Set bk=2pkmk. The sum over n of 2np times the first term in step 3.1, apart from its constant, is nZ(j02(pr)j/rbn+j1/r)r. For nonnegative numbers x_j and summable nonnegative weights w_j, finite Hölder gives (jwjxj)r(jwj)r1jwjxjr. Here wj=2(pr)j/r has finite sum W_r because p>r. Applying this inequality, then summing over n, bounds the display by Wrrkbk. One may first use finite sets of n,j and then increase them: every summand is nonnegative and each shifted sum of the finitely supported b is at most kbk. Likewise the second term gives nZ(j12(sp)j/sbnj1/s)sWsskbk, where Ws=j12(sp)j/s< since s>p. These are two explicit geometric sums, not an interpolation theorem used as an undeclared supplier.

F4step 3.1
5.1

Apply F1 to Af and split the positive t-axis into [2n,2n+1). Monotonicity of its level-set measure yields Afpp(2p1)n2npm{Af>2n}. The nonnegative sum is valid whether or not finiteness is known initially. Steps 3.1 and 4.1 bound it by Ck2pkmkCfp, because f>2k on E_k. Thus the strong bound is established for every finite-support simple f, with C depending only on r,p,s and the two restricted constants, not on the finite family or selector.

F1step 3.1step 4.1
6.1

For general fLp(R), the same finite model formula makes sense: each packet belongs to Lp and Lp by its Schwartz decay, so F4 makes the coefficients absolute. More explicitly an exponent M with Mp>1 and Mp'>1 gives integrable powers of the packet bound. For every integer j1 define Qj(t)=sgn(t)jmin(t,j)/j, with sgn(0)=0, and put fj=1[j,j](Qj(Ref)+iQj(Imf)). Each component of fj takes only the 2j2+1 values k/j with kj2, so fj is a simple measurable function supported on a finite-measure set. Componentwise rounding toward zero gives fjf, while fjf almost everywhere. Hence fjfp2pfp, and F5 gives fjfp0. Hölder gives convergence of every packet coefficient; more strongly, the triangle inequality and selector bound give AfjAfpuSϕupϕupfjfp0. This finite auxiliary sum may depend on S; it is used only to identify the limit, not in the uniform estimate. Pass to the limit in step 5.1 using Minkowski's norm continuity to obtain the stated uniform strong bound. Empty finite families, zero simple functions and zero endpoint constants are covered without division by them. The strict inequalities r<p<s are exactly what makes the two geometric weights summable; no endpoint weak-to-strong inference is made. AC is inherited as identified in F6. This conditional interpolation proof does not assume that the Hunt restricted estimates have already been proved.

F3F4F5F6step 5.1
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Carleson maximal operator is strong ltwo

Statement

Assume AC. The one-sided real-line Carleson maximal operator extends boundedly to complex L2(R).

Facts & Assumptions

[F1]

Uniform finite-model strong Lp estimates transfer to the real-line maximal operator on Schwartz input Wave packet model dominates the linearised carleson operator.

[F2]

Finite models are uniformly restricted weak type (q,q) for every 1<q<infinity Hunt exceptional set and distribution estimates.

[F3]

Restricted weak bounds at 1<r<p<s<infinity give a uniform strong(p,p) finite-model bound Carleson restricted weak interpolation.

[F4]

The real-line operator on Schwartz functions is the supremum of the absolute values of linear one-sided Fourier cutoffs Carleson operator and measurable linearisation.

[F5]

Schwartz classes are dense in complex L2 under countable choice Schwartz space is dense in L2.

[F6]

Complex L2 is complete under countable choice and norm convergence has an almost-everywhere convergent subsequence Complex completeness, density, and inner product: the consumer interface.

[F7]

Assume AC The Axiom of Choice, supplying the countable choice in F5 and F6 and the inherited analytic interfaces.

Proof

Given: The stated AC assumption and the exact one-sided operator of F4.

1.1

Apply F2 at r=3/2 and s=3, then F3 with p=2. These strict endpoint exponents give a finite-model strong L2 constant independent of the family and selector. F1 gives CRu2Ku2 for every complex Schwartz u, for a fixed finite K. This uses estimates on both sides of two; no restricted weak-L2-to-strong-L2 inference is made.

F1F2F3
2.1

For Schwartz u,v, linearity of every cutoff and abab imply pointwise CRuCRvCR(uv). The suprema are finite because the Schwartz transform is integrable. Thus step 1.1 gives CRuCRv2Kuv2. Also CR(zu)=zCRu and CR(u+v)CRu+CRv pointwise.

F4step 1.1
3.1

For each fixed fL2, F5 and the countable choice supplied by F7 give Schwartz uj with ujf2<1/j. Step 2.1 makes (CRuj) Cauchy in L2, so F6 gives a limit; define CRf to be this class. If v_j is another such sequence, the same Lipschitz inequality bounds the distance between their output sequences by Kujvj20, so the limit is independent of the approximation. On a Schwartz class take the constant sequence to see agreement with the original operator. Passing norms to the limit gives CRf2Kf2.

F5F6F7step 2.1
4.1

Applying the same argument to approximants for f,g gives CRfCRg2Kfg2, so the extension is continuous and is the unique continuous extension from the dense Schwartz classes. It is nonnegative almost everywhere: F6 supplies an a.e.-convergent subsequence of its nonnegative approximating outputs. Homogeneity and subadditivity also pass from step 2.1; for subadditivity take subsequences along which the three output sequences for u_j, v_j and u_j+v_j converge a.e., successively using F6, and pass the pointwise inequality off the finite union of exceptional null sets. The zero class maps to zero. This is the required bounded maximal-operator extension on complex L2, with precisely the inherited AC assumption and no simultaneous arbitrary-index choice of approximants.

F6F7step 2.1step 3.1
LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson real line to torus transfer

Statement

Assume AC. Uniform real-line Carleson Lp bounds for 1<p<infinity imply the symmetric Fourier partial-sum maximal Lp bound on T with normalized Haar measure.

Facts & Assumptions

[F1]

Real-line one-sided cutoffs and their maximal operator have the stated normalized Fourier integral convention on Schwartz input Carleson operator and measurable linearisation.

[F2]

The fixed nonzero Schwartz packet phi has transform psi supported in [-1/8,1/8] Carleson tiles wave packets and tile order.

[F3]

The torus is R/Z with characters ek(x)=e2πikx, coefficients integrated on [0,1], and SNf=kNf^(k)ek Period-one Fourier coefficients, partial sums, and convolution on the torus.

[F4]

Schwartz Fourier inversion holds pointwise Fourier inversion on Schwartz space.

[F5]

Under countable choice, Fejer means converge in complex Lp(T) for1<=p<infinity Fejer means converge in L^p for 1 <= p < infinity.

[F6]

Each Fejer mean is the finite average of the partial sums and hence a trigonometric polynomial Cesaro and Abel means of a Fourier series.

[F7]

Complex Hölder and Minkowski hold Complex Holder, Minkowski, and the quotient norm.

[F9]

Fubini applies to absolutely integrable functions on sigma-finite products Fubini's theorem for L^1 functions on a sigma-finite product.

[F10]

Increasing nonnegative integrands pass to the integral limit Monotone convergence for the integral.

[F11]

Assume AC The Axiom of Choice, supplying the countable choice in the Fourier and Fejer interfaces.

Proof

Given: Fix 1<p<infinity and suppose CRuLp(R)DpuLp(R) for every Schwartz u. This weaker Schwartz-input hypothesis suffices for the asserted transfer.

1.1

Let P(x)=kmake2πikx be a trigonometric polynomial. Its Fourier coefficients are a_k: the integral of e2πi(kj)t on [0,1] is one for k=j and zero otherwise, by direct integration for the nonzero integer k-j. For 0<epsilon<1 put uε(x)=ϕ(εx)P(x), a Schwartz function: the derivatives of P are bounded and the scaled phi and all its derivatives decay to every order, so the Leibniz formula proves every Schwartz seminorm finite. Direct substitution in the absolutely convergent Fourier integral gives uε^(ξ)=kmakε1ψ((ξk)/ε). The kth summand is supported in [kε/8,k+ε/8]. Thus for every integer N>=0 the interval [N1/2,N+1/2] contains exactly the entire summands with |k|<=N and misses the other ones. F4 then gives the exact identity TN+1/2uε(x)TN1/2uε(x)=ϕ(εx)SNP(x). The half-integer cutoffs avoid every endpoint frequency; no half-weight term occurs.

F1F2F3F4given
1.2

We prove the precise periodic averaging limit used below. Let w be continuous, nonnegative and bounded by C(1+x)2, and let G be continuous and one-periodic. Then εRw(εx)G(x)dx(Rw)01G(t)dt. Decomposing the line into n+[0,1) and substituting gives the left side as 01G(t)Rε(t)dt, where Rε(t)=εnZw(ε(n+t)). This rearrangement is absolute: G is bounded, w<, and F8 followed by F9 applies. These Riemann sums converge uniformly for t in [0,1] to w. To verify uniformity, partition the line into cells [ε(n+t),ε(n+t+1)). For cells meeting [-R,R], the difference between the left-endpoint sum and the integral is at most (2R+2)ωR(ε), where ωR is the modulus of continuity of w on [-R-1,R+1], and 0<epsilon<=1. For the remaining cells the sum and integral of the decay majorant are at most C/(1+R), uniformly in t and epsilon, by comparison of the monotone tails of (1+x)2 with their integrals. First choose R large and then epsilon small. The resulting uniform convergence allows integration against bounded G and proves the displayed limit.

F8F9
2.1

For every finite cutoff bound J, the identity in step 1.1 implies ϕ(εx)max0NJSNP(x)2CRuε(x). Raise to p, integrate and use the assumed real-line bound to obtain εRϕ(εx)p(max0NJSNP(x))pdx(2Dp)pεRϕ(εx)pP(x)pdx. Both periodic factors in this display are bounded and continuous.

F1step 1.1given
3.1

Apply step 1.2 with w=ϕp. Its hypotheses hold by Schwartz decay and continuity, choosing a decay exponent M with Mp>=2. Also 0<ϕp<, since phi is a nonzero continuous Schwartz function. Taking epsilon to zero in step 2.1 and dividing by this positive window integral yields max0NJSNPLp([0,1])2DpPLp([0,1]). This has normalized Haar measure exactly: the periodic averaging limit contains 01, with no interval-length factor. The constant is independent of J and the degree of P.

F2step 1.2step 2.1
4.1

For any fLp(T), normalized measure and F7 give f1fp, so its coefficients and partial sums in F3 are defined. By F5 and F6, the polynomials Pj=σjf converge to f in Lp. For each fixed J, max0NJSN(Pjf)(2J+1)Pjf1(2J+1)Pjfp0, because every Fourier coefficient difference has magnitude at most its L1 norm and every character has modulus one. Hence the finite maxima for P_j converge uniformly to the finite maximum for f. Step 3.1 and Minkowski's norm continuity give the same bound 2Dpfp for that finite maximum. Finally these nonnegative maxima increase as J tends to infinity; F10 gives supN0SNfLp(T)2DpfLp(T). Their countable supremum is measurable. The zero input and zero polynomial cases follow directly, and N=0 was included in the exact cutoff identity. Apply this argument separately to each p strictly between one and infinity. AC is inherited through F11; the approximants here are the explicitly specified Fejer means. This proves the conditional transfer without assuming that the real-line bound has already been established elsewhere.

F3F5F6F7F10F11step 3.1
TheoremStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-5.6-terra)Open item page →

Carleson hunt maximal inequality on the torus

Statement

Assume AC. For every 1<p<infinity, ||sup_{N>=0}|S_N f|||{Lp(T)}<=C_p||f||{Lp(T)} for all complex f in Lp(T), with normalized measure.

Facts & Assumptions

[F1]

The finite models are uniformly restricted weak type (q,q) for every1<q<infinity Hunt exceptional set and distribution estimates.

[F2]

Restricted weak bounds at1<r<p<s<infinity give uniform strong(p,p) finite-model bounds Carleson restricted weak interpolation.

[F3]

Uniform finite-model strong(p,p) bounds imply the real-line Carleson maximal bound on Schwartz input Wave packet model dominates the linearised carleson operator.

[F4]

Real-line bounds on Schwartz inputs imply the symmetric partial-sum maximal bound on normalized Lp(T), including all complex inputs and the infinite supremum Carleson real line to torus transfer.

[F5]

The torus convention is period one, f^(k)=01f(t)e2πiktdt and SNf=kNf^(k)e2πikx Period-one Fourier coefficients, partial sums, and convolution on the torus.

[F6]

Assume AC The Axiom of Choice, supplying the countable-choice Fourier, maximal and Fejer interfaces used by these suppliers.

Proof

Given: An arbitrary exponent1<p<infinity and a complex class f in Lp(T) with the normalized measure and coefficients of F5.

1.1

Choose r=(p+1)/2 and s=2p, so1<r<p<s<infinity. Apply F1 at r and s, then F2. The resulting finite-model strong(p,p) constant B_p depends only on p and the fixed packet, not on the finite family or measurable selector.

F1F2given
2.1

F3 transfers this uniform bound to CRupDpup for every complex Schwartz u, with Dp=κ1Bp< and the positive reconstruction constant defined there. No passage from weak type at the same exponent is used. Since p was arbitrary, these real-line bounds hold separately for every exponent strictly between one and infinity.

F3step 1.1
3.1

Apply F4 at the chosen p. It gives a finite constant Cp, depending only on p and the real-line bound Dp, such that supN0SNfLp(T)CpfLp(T) with precisely the coefficients and normalized measure in F5. The countable supremum is measurable, and the estimate holds for the actual partial sums of every complex Lp representative class; changing the representative does not change any coefficient. The case f=0 and the cutoff N=0 are included by F4. The argument uses no p=1 or p=infinity assertion. AC is inherited exactly through F6 and its suppliers.

F4F5F6step 2.1

5 · Examples, counterexamples and false statements

None yet.

Sources