Fourier Restriction Theory
Daniel Naylor

Contents

1What is Fourier Restriction Theory?
2Exponential sums in Lp
3Introduction to Fourier Transform
3.1Fourier Transform on n
4Introduction to Fourier Restriction
5Equivalent Versions of Fourier Restriction
6Tube incidence implications of Fourier restriction
Index

1 What is Fourier Restriction Theory?

Main object: f:d, f(x)=ξbξe2πixξ, bξ.

Notation. We will write e(xξ)ie2πixξ.

xd is a spatial variable, and ξd is the frequency variable.

The frequencies (or Fourier transform) of f is restricted to a set R (where R we will always be finite – so no need to worry about convergence issues).

Goal: Understand the behaviour of f in terms of properties of R.

Example.

Both avoid linear structure.

{logn} is a concave set (getting closer and closer together).

{(n,n2)} lie on a parabola.

Guiding principle: if properties of an object avoid (linear) structure, then we expect some random or average behaviour.

The above examples avoid linear structure using some notion fo curvature. See Bourgain Λ(p) paper: extra behaviour.

Square root cancellation: If we add ±1 randomly N times, then we expect a quantity with size N12.

Theorem 1.1 (Khinchin’s inequality). Assuming that:

  • {𝜀n}n=1N be IID random variables with (𝜀n=1)=(𝜀n=1)=12

  • 1<p<

  • x1,,xN

Then
(  ||N∑      ||p) 1p    (∑N     ) 12
  𝔼||   𝜀nxn||    ∼p     |xn|2   = ∥xn∥2.
   |n=1    |        n=1

Notation. p means but the constant may depend on p.

Proof. Without loss of generality, x1,,xn. Without loss of generality, x2=1.

p=2: want to show 𝔼(|n𝜀nxn|2)1.

 (        --------)
𝔼  ∑  𝜀nxn∑  𝜀mxm   = ∑  𝔼(𝜀n𝜀mxnxm-) = ∑ |xn|2 + ∑  ∑  xnxm--𝔼𝜀n𝔼𝜀m.
    n      m          n,m                n         n m=n     ◟◝=◜0◞
What about general exponents p?
 ( ||      ||p)   ∫ ∞  ( ||       ||p    )
𝔼  ||∑  𝜀nxn||   =     ℙ  ||∑   𝜀nxn || > α  dα.
   |n     |      0     |n      |
The equality here is the Layer cake formula, which is true for any p(0,).

Let λ>0. Study the random variable eλn𝜀nxn(0,).

                (         )
  (  ∑     )      ∏           ∏           ∏  (1       1     )
𝔼  eλ n 𝜀nxn  = 𝔼     eλ𝜀nxn  =    𝔼eλ𝜀nxn =     2eλxn + 2e−λxn  .
                   n           n           n
Fact: 12ez+12ezez22 (to check, use the Taylor series). So we can get

α(eλn𝜀nxn>α)𝔼(eλn𝜀nxn)(Chebyshev’s inequality)neλ2|xn|22=eλ22

By symmetry,

αℙ(e||λ ∑n𝜀nxn|| > α) ≲ eλ2∕2.
Choose α=eλ2:
  (|       |   )     (|       |p    )
   ||∑      ||          ||∑      ||    p     −λ2∕2
ℙ  ||   𝜀nxn|| > λ = ℙ  ||   𝜀nxn|| > λ   ≲ e    .
     n                  n
Use in Layer cake:
 ( ||       ||p)   ∫ ∞
𝔼  ||∑  𝜀 x ||  ≲     e−α2∕p∕2dα ∼  1.
   | n  n n|     0             p
Lower bound: use Hölder’s inequality. X=n𝜀nxn.
    --        p 1∕p     q1∕q
𝔼◟(X◝◜X)◞ ≤ (𝔼◟-(|X◝|◜))-◞(𝔼|X | )  .
  =1        ≲p1
1p+1q=1.

Can you find a more intuitive proof? E-mail Dominique Maldague.

Corollary. 𝔼(|n=1N𝜀nfn(x)|pdx)p|n=1N|fn(x)|2|p2dx.

Useful for exercises!

Return to Fourier restriction context.

f(x)=n=1Ne(nx)R={1,,N}g(x)=n=1Ne(n2x)R={12,22,,N2}

Both f,g are 1-periodic. So study them on 𝕋=[0,1]. f(0)=N, |f(x)|N for [0,c1N]. g(0)iN, |g(x)|N for x[0,c1N2].

∫             ∑  ∫
    |f(x)|2dx =        e((n− mx )dx = N).
 [0,1]          n,m [0,1]
∫           ∑  ∫
    |g(x )|2 =        e((n2 − m2 )x)dx = N.
 [0,1]        n,m [0,1]

PIC

∫
    |f(x)|pdx ≥ N p∕2 + N p−1
 [0,1]
∫
    |f(x)|pdx ≥ N p∕2+ N p−2.
 [0,1]
For the first one, Np1 (organised behaviour) dominates as soon as p>2, and for the second one, Np2 dominates for 2p4 (“square root cancellation behaviour lasts for longer”).

2 Exponential sums in Lp

Recall: studying f(x)=ξRe(ξx). When R does not have (linear) structure, expect |f(x)||R|12 (“sqrt cancellation”) in an appropriate sense (in Lp), more than when R is structured.

Linear R f(x)=n=1Ne(nx) vs convex R g(x)=n=1Ne(n2x).

Have

∫                  ∫
     |f(x)|2dx = N =      |g(x)|2dx.
 [0,1]               [0,1]
Have |f(x)|N on [0,cN1], and |g(x)|N on [0,cN2].

PIC

L2 does not distinguish f,g. L does not distinguish. However, 2<p< does.

What about 1p2? We don’t usually study this range because the estimates tend to be trivial / not interesting.

Focus on 2<p<. Preduct size of

∫      p
    |f|.
 [0,1]

Square root cancellation lower bound:

     ∫            ( ∫        ) 2p
           2 H ölder’s       2⋅p2     □
N  =  [0,1]|f |  ≤      [0,1]|f|     (1)
Np2[0,1]|f|p.

Constant integral lower bound:

[0,1]|f|p[0,cN1]|f|pNpN1[0,1]|g|p[0,cN2]|g|pNpN2

|f|pNp2+Np1, |g|pNp2+Np2.

Note that for the f bound, Np1 is bigger than Np1 (as long as p>2), so the constant integral dominates!

For g, if 2p4, the square root cancellation dominates, but for p>4 the constant integral takes over.

Assuming:

Theorem 2.1. - p>2 - bn Then:

∫ ||         ||p
  ||∑  bne(nx)|| ≤ N p2−1∥bn∥p.
  | n       |            2

Proof. Consider: n=1Nbne(nx), bn.

[0,1]|nbne(nx)|pdxnbne(nx)p2[0,1]|nbne(nx)|2dx(N12bn2)p2bn22CS=Np21bn2p

Note that this is sharp when bn1.

n=1Nbne(n2x). Focus on p=4.

    |          |4
∫   ||∑       2 ||      ∑  ∑  ∑  ∑       ------∫       2   2   2    2
 [0,1]||   bne(n x)|| dx =             bn1bn2bn3bn4 [0,1]e((n1 + n2 − n3 − n4)x)dx.
      n                n1  n2 n3 n4
The integral vanishes unless n12n32=n42n22.

Number Theory lemma: If m, then

# divisors of m ≲ logm.
Follows from unique prime factorisation.

Warning. The above lemma is false.

See correction later.

For fixed n1,n3,

            2   2
# {◟(n2,n4) : n1 −-n3-=◝◜(n4 +-n2)(n4-−-n2)}◞≲ log N.
                =:𝒮n1,n3
Hence

[0,1]|nbne(n2x)|4dxn1n3|bn1bn3||(n2,n4)Sn1,n3bn2bn4|(logN)bn24

We will now use to mean up to powers of logN.

2<p<4: 1p=𝜃2+1𝜃4, 𝜃[0,1], h=nbne(n2x), hph2𝜃h41𝜃bn2𝜃bn21𝜃bn2.

p4:

∫
      p      p− 4   4 CS   12     p−4    4    p2−2    p
 [0,1]|h| ≾ ∥h∥∞ ∥bn∥2 ≤ (N  ∥bn∥2)  ∥bn∥2 ≈ N   ∥bn∥2.

Assuming:

Theorem 2.2. - 2>p Then:

∫ ||∑          ||p
  ||   bne(n2x)|| dx ≾ (1 = N p2− 2)∥bn∥p2.
  |n         |

Sharp by bn1.

Positive take away: estimates are sharp, proofs are elementary. Easy to think of sharp examples.

Number Theory counting idea shows:

[0,1]2|u(x,t)|6dxdtbn26u(x,t)=|n|Nnbne(nx+n2t)[0,1]3|u(x,t)|4dxdtbn24u(x,t)=|n|Nn2bne(nx+|n|2t)

Sharp, 6,4=pcrit.

Strichartz estimate for periodic Schrödinger equation, observed by Bourgain in 1990s.

Negatives: on 𝕋3 pcrit=103, but this technique can only work on even integer values of p.

𝕋1, 𝕋2 only sharp Strichartz estimates per Schrödinger until 2015!

2015: Bourgain-Demeter proved (l2,Lp) sharp decoupling estimate. Gives sharp Strichartz estimate for Schrödinger in 𝕋d for all d.

Proved earlier:

          |           |
    ∫     ||∑    ( n2 )||p           p      p
N −2    2 ||   e  N-2x || dx ≤ (1+ N 2−2)∥bn ∥2.
     [0,N ] n∼N
(where nN means Nn2N).

Conjecture:

  ∫    |           |p
−      ||∑   b e(a x)|| dx ≲ (1 + N p2−2)∥b ∥p
   [0,N2]||n∈N  n  n  ||                 n 2

an[0,1], an+1an1N, an+2an+1(an+1an)1N2.

Example: {an}{n3N3}n=N2N.

3 Introduction to Fourier Transform

Correction for lecture 2: Number Theory Lemma.

True statements:

 1  ∑
--      # ÷ (n) ≲ logN,
N 1≤n≤N
and #÷(n)𝜀n𝜀.

See Terence Tao notes online.

Explaining #÷(n)𝜀n𝜀: 𝜀>0, c𝜀(0,) such that #÷(n)C𝜀n𝜀 for all n1.

We will be using to mean “ but up to sub-polynomial in n”.

Question:

  ∫    ||           ||p
−      ||∑   bne(anx)|| dx ≾ (1 +N p2− 2)∥bn∥2.
   [0,N2]|n∼N        |                   2
For example, an=n2N2.

Recall: nN means Nn2N.

{an}[0,10], an+1an1N, (an+2an+1)(an+1an)1N2.

Reasonable conjecture?

Yes, reasonable.

Khinchin’s inequality: May select bn{±1} so that

 ∫     |          |p      ∫     |       |p2
       ||∑         ||             ||∑     2||
− [0,N2]||   bne(anx)|| dx ∼ − [0,N2]||   |bn| || dx.
         n                        n

Constant integral: bn1. (1,1,1,,1).

  ∫     ||∑        ||p        ∫
−       ||   e(anx)|| dx ≳ -12    N pdx ∼ N p−2 = N p2− 2∥bn∥p2.
   [0,N2]|n       |      N   [0,c]

Warning. Enemy scenario: {an}={nN}n=N2N (technically {3NnN} if we want to satisfy the conditions mentioned above).

(bn)=(1,0,,0,1,0,,0,1,,0). Length N vector with N12 many 1s. Have:

       |                  |
 ∫     ||  ∑      ( ∘ -2- )||p      ∫     || ∑    (     ) ||p
−      ||        e    m-x  || dx = −      ||     e √m--x  ||dx
  [0,N2]|N≤m2≤2N      N    |        [0,N2]|m2∼N     N    |

PIC

We can calculate that the above expression is in fact Np212 (which breaks the conjecture until p>6).

It turns out that this is (roughly speaking) the only problem.

Why do we care?

bn1, p=4. Then

       |        |
 ∫     ||∑       ||4       2       2
−     2||   e(anx )|| dx ≾ N  = |{an}| .
   [0,N ]  n
=⇒  #{a  + a   = a  + a  } ≲ |{a }|2.
       n1   n2    n3   n4      n
Convex sequences have minimal additive energy.

Decoupling doesn’t know how to take advantage of bn1.

3.1 Fourier Transform on n

f:n, fS(n) Schwartz function: xαβf< for all α,β.

      ∫
ˆ         −2πix⋅ξ
f(ξ) = ℝn e     f(x)dx.
x is the spatial variable, and ξ is the frequency variable.

Facts:

Basic question about f^: Lp to Lq boundedness?

Plancherel’s:

      ∫   -- ∫   --
∥^f∥22 =  f^^f =   ff = ∥f∥22
(isometry on L2).

p=1, q=:

||∫             ||  ∫
||  e−2πix⋅ξf (x)dx|| ≤   |f(x)|dx = ∥f∥1
(contraction from L1 to L).

By interpolation (Marcinkiewicz): f^qfp for 1p2, 1p+1q=1 (Hausdorff-Young inequality).

Are there any other (p,q) for which

∥f^∥q ≲p,q ∥f∥q?

Attempt 1: Let φCc(n) (compactly supported smooth function on n), with suppφB1(0).

Consider f(x)=k=1N𝜀kφ(xvk).

Choose vk so that {B1(vk)} are not overlapping. Then

      ∫ ||∑            ||p  ∑  ∫
∥f∥pp =  ||   𝜀kφ(x− vk)|| =      |φ(x− vk)|pdx = N ∥φ∥pp.
        | k           |    k
Also
       N∑                ( ∑N          )
f^(ξ) =    𝜀ke− 2πiξvk^φ(ξ) =     𝜀ke−2πiξvk φ^(ξ).
       k=1                 k=1
We will use Khinchin’s inequality.

f^qN12φ^q.

4 Introduction to Fourier Restriction

Theorem (Hausdorff-Young inequality). Assuming that:

  • 1p2

  • 1p+1q=1

Then
 ^
∥f∥q ≤ ∥f∥p.

Proof. The inequality is true for p=1, q= and for p=2, q=2 the inequality is true since we have equality (Plancherel).

For values in between we can interpolate.

Are there any other (p,q) for which

∥f^∥q ≲ ∥f∥p?                                  (∗)

We saw that 1p2 was necessary (translations / modulations, Khinchin’s inequality).

Scaling: Plug in fλ(x)=f(λx) which is L-normalised (fλ=f). Then fλ^(ξ)=λnf^(λ1ξ) which is L1-normalised (fλ^1=f^1).

              ∫                   ∫
(LHS  of (∗))q =   |^fλ(ξ)|qdξ = λ−nq+n  |^f(ξ)|qdξ.
               ℝn
              ∫                 ∫
          p            p     − n       p
(RHS  of (∗)) = ℝn |fλ(x)| dx = λ   |f(x)| dx.
So we need for all λ>0:
 −n+nq ^     − np
λ     ∥f∥q ≲ λ  ∥f∥p.
So we need n+nq=np, i.e. 1p+1q=1.

Classical questions

What is Cp,q the smallest constant such that f^qCp,qfp?

Which functions f satisfy f^qfp=Cp,q?

2014:

 ^
∥f∥q ≤ Cp,q∥f ∥p − distp(f,maximisers (Gaussian)).
Fourier restriction asks which (p,q) permit estimates f^Lq(R)fLp(n) (R is the restricted frequency set, Rn).

Example. R=B1(0)n, unit ball : Basically, sitll governed by Hausdorff-Young inequality.

Example. R=B1(0){xn=0} (measure 0 subset of n).

RR(pq) means

                           n
∥^f∥Lq(ℛ ) ≲ ∥f∥Lp(ℝn)   ∀f : ℝ → ℂ Schwartz
Always: RR(1) true for all R.

In the second example, only this trivial statement is true (i.e. RR(pq) is false for all other values for p,q).

Let R=Sn1 be the unit sphere in n. Consider

∥^f∥ q n−1 ≲ ∥f∥ p  n
   L (S   )     L (ℝ )
where Lq(Sn1) uses the usual surface measure dσ on Sn1.

Notation.

^^ˇx↦→− f◟x(◝x◜)◞       f◟^◝(x◜)◞        f◟(◝x◜)◞       f◟(◝x◜)◞
   ∈𝒮(ℝn)      ∈𝒮(ℝn)      ∈𝒮(ℝn)     ∈ 𝒮(ℝn)
We may call ˇ the “inverse Fourier transform”.

Let φCc(n), -valued, 12 on B1(0), with support in B2(0).

May also assume ˇφ is -valued, ˇφ1 on Bc(0). ˇφ bounded, |ˇφ(x)|m(|x|2+1)mm. |ˇφ(x)| behaves like χB1(0) in Lp.

PIC

Consider dilates fR(x)=ˇφ(R1x)e2πixvr, R1.

Frequency side |fR^(ξ)|RnχBR1(0)(x) (L1-norm).

PIC

∫
     |^f (ξ)|qdσ(ξ) ∼   RnqR −(n− 1)  .
 Sn−1  R             ◟---◝◜---◞n−1
                   →σ(BR−1(n)∩S   )
BR1(n)Sn1 cap of radius R1. Spacial side |fR(x)|χBR(0)(x) (in L-norm).

PIC

height 1, n|fR(x)|pdxRn.

Rnn1qRnp, nn1qnp.

Consider: gR(x)=eixwrˇφ(R1x1,,R1xn1,R2xn).

Frequency: |gr^(ξ)||cylinder|1χcylinder(ξ). |cylinder|1| (R1R1R2)1=Rn+1.

PIC

∫
     |^g (ξ)|qdσ(ξ) ∼ R (n+1)qσ (cap of radius R−1) = R(n+1)q−(n−1).
 Sn−1  R

Spatial side: |gR(x)|χcylinder(x) (L-norm).

PIC

|gR(x)|p=Rn1+2=Rn+1.

Rn+1n1qRn+1q, n+1n1qn+1p. Implies B.

On Monday, we will build examples h such that h^ sees all of Sn1.

R=Sn1Rn.

Consider the statement

∥^f∥Lq(Sn−1) ≲ ∥f∥Lp(ℝn).
(recall that we called this RSn1(pq)).

Fix φCc(n), φχB1(0). For computing Lp norms, |φ^|χBc(0).

Wave packet function with localised spatial and frequency behaviour.

Last time: fR(x)=eixvRˇφ(R1x1,,R1xn1,R2xn).

Frequency: |fR^(ξ)|

PIC

Sn1|fR^|qdσ

|-------------------|
n + 1− n-−-1 ≤ n+-1-|
---------q-------q---

Spatial: |fR(x)|

PIC

1|fR|p

Note: sphere near n looks like (ξ1,112|ξ|2), Sn1suppfR^cap of radius R1.

PIC

Naive attempt: gR(x)=Rnˇφ(Rx)

Frequency: |gR^(ξ)|

PIC

Sn1|gR^(ξ)|qdξ1

Spatial: |gR(x)|

PIC

|gR|pRnRnp.

Deduce: 1Rnp+n, so

|-----|
-p ≥-1|
(trivial).

|gR^|1 on Sn1 made gR^Lq(Sn1) easy to compute.

Could we improve things?

Could think about R1: then gR^Lq(Sn1)1, gRp1. This is more efficient, but we can’t take a limit. So not so useful.

Build a function H(x) which satisfies |H^(ξ)|1 on Sn1.

Let {𝜃} be a maximal collection of R1-spaced points on Sn1 (#{𝜃}Rn1).

PIC

For each 𝜃, let A𝜃1:nn be an affine map which sends

B1(0) → R−1 × ⋅⋅⋅ × R−1 × R −2 ellipsoid centered at 𝜃, tangent to Sn− 1 at 𝜃.
Define φ𝜃=φA𝜃, H(x)=𝜃ˇφ𝜃(x).

H^(ξ)=𝜃φ𝜃(ξ)1 on Sn1 (actually on R2-neighbourhood of Sn1).

Sn1|H^(ξ)|qdσ1.

H(x)=𝜃ˇφ𝜃(x).

|φ𝜃(x)| Frequency:

PIC

|ˇφ𝜃(x)| Spatial:

PIC

esssuppH𝜃esssuppˇφ𝜃.

PIC

“bush of tubes”

Rn1 many R××R×R2 tubes in R1-separated directions

∫              ∫  ||∑              ||p
    |H (x)|pdx =    ||      ˇφ 𝜃(x)    ||dx
  ℝn            ℝn|| 𝜃    ◟-◝◜ ◞   ||
                      ℂ- valued function

PIC

Compute

n|𝜃χT𝜃(x)2|p2=n|𝜃χT𝜃(x)|p2=munder∫   |      |p
    ||∑  χ  ||2
 BR      T𝜃
◟-----◝◜----◞ (Rn1) p 2 Rn +

                                                    ∫       |      |p
                                                            ||∑   χT ||2
                                                     RR2∖BR       𝜃
                                                    ◟-------◝◜-------◞ ()

Consider overlap of T𝜃 on λSn1 (λ(R,R2)).

Average overlap on λSn1:

  ∫     ∑              ∑  ∫                  ∑
−          χT𝜃 ∼ λ −(n−1)        χT𝜃 ∼ λ−(n−1)   Rn−1 = λ−(n− 1)R2(n−1).
   λSn−1 𝜃              𝜃  λSn−1              𝜃

PIC

Not too hard to check that the number of active T𝜃 on λSn1 is λ(n1)R2(n1).

Now calculate:

       ∑    ∫          ||∑     ||p2
(∗) ∼                  ||   χT𝜃||    dx.
     R<λ<R2  λ<|x|<2λ   | 𝜃    |
                     −(◟n−1)-◝◜2(n−1◞)p n
                    [λ     R    ]2λ

     −(n+1)p  2n    (n− 1)p  n
1 ≲ R      [R   +R     2R  ].
Two cases: either R2n dominates or the other term dominates. So p2nn+1.

5 Equivalent Versions of Fourier Restriction

Searching for (p,q) for which

∥^f∥Lq(Sn−1) ≤ Cp,q,n∥f∥Lp(ℝn).

  • (1)

    PIC

    R=[0,1]n1×{0}

    PIC

  • (2)

    PIC

    BR2|𝜃χT𝜃|p2. “continuum incidence geometry problem”.

    PIC

    integral equals =BRBR2BR|𝜃χT𝜃|p2.

    “fractal geometry”.

    PIC

Reminder: RSn1(pq) means

  ^                                  n
∥f ∥Lq(Sn−1) ≤ Cp,q,n∥f∥Lq(ℝn)   ∀f ∈ 𝒮(ℝ ).

Conjecture (Restriction Conjecture). RSn1(pq) if and only if n+1n1qn+1p and p<2nn+1.

First proved for S12 by Fefferman (1970) and Zygmund (1974).

Special things happen in 2, classical harmonic analysis techniques apply.

Same conjecture for RPn1(pq), where

  n−1        2    n
P    = {(ξ,|ξ| ) ∈ ℝ : |ξ| < 1}.
f^Lq(Pn1)q=|ξ|<1|f^(ξ,|ξ|2)|qdξ.

PIC

For n3, open and active!

Restriction theory can be used to deduce continuum incidence geometry estimates.

Surprisingly, we can go the other way too (very recent progress, whereas the above direction has been well-known since at least the 90s).

Equivalent formulations of RPn1(pq)

Dual version is called “Fourier extension”:

                        ||∫                  ||
∥f^∥ q  n−1 =     sup    ||       ^f(ξ,|ξ|2)g(ξ)dξ||.
   L (P   )   g∈Lq′(Pn−1) ||  |ξ|<1n−1            ||
             ∥g∥Lq′(Pn−1)=1  ξ∈ℝ
(1q+1q=1). The integral equals:

Math input error

Call the last inequality RPn1(qp).

Local, dual version: allows us to work with functions, F.T.

For any R1, any BRn, we have

(∫        ′)p1′    1( ∫      ′  ) 1q′-
     |f(x)|p    ≲  Rq    |^f(ξ)|qdξ
  ℝn
for all fS(n) with suppf^NR1(Pn1).

Call this RPn1,loc(qp).

RPn1(qp)Rpn1,loc(qp). First thing bounds Eg, while secound thing bounds f when we have suppf^NR1(Pn1).

Let fS(n), suppf^NR1(Pn1). Try to express f in trems of ext. op.

Math input error        ∫    ( ∫               ) p′′-
  −1 p′−1                 ′ q′ ′  q
(R   )    I −1   |ξ′|<1|gξn (ξ )| dξ    d ξn
◟---------R-------◝◜-----------------◞
()
using RPn1(q p) Goal is to bound this last expression by
   p′∫    ∫            ′
R− q           |gξn(ξ′)|qdξ′d ξn
    ◟IR−1-|ξ′|<1-◝◜-----------◞
         ∥^f∥Lq′(N   (Pn−1))
               R−1
Lucy case: pq<1, i.e. 1<qp. Then
      − p′+1∫
(∗) ≤ R
            IR−1

                        1
∫   H ölder’s   1− 1( ∫  s) s
 A h  ≤   |A|  s   Ah    ,
s1 (actually since h approximately constant on A)

RPn1(qp)RPn1,loc(qp) continued.

Spatial: x,f(x),Eg(x)

∫-----------|
|   |f(x)|p′dx|
-ℝn----------
∫         ′
   |Eg (x)|pdx
 ℝn

PIC

Frequency ξ:

PIC

g(ξ,|ξ|2)=g(ξ), suppf^NR1(Pn1),

∫               ′
          |^f(ξ)|qdξ
 𝒩R−1(𝒫n−1)
∫       ′ q′ ′
  ′  |g(ξ )|d ξ.
 |ξ|<1

                                                     ′
∫         ′       (∗)     ′  ∫    ( ∫           ′   )pq′
    |f(x)|pdx ≤ ⋅⋅⋅≤  R−(p−1)            |gξn(ξ′)|qdξ′   dξn.
 BR                          IR−1   |ξ′|<1

Aiming for

          (                     ) p′
      − p′- ∫    ∫         ′ q′  ′ q′
(∗) ≤ R q         ′   |gξn(ξ)| dξ    .
            IR−1 |ξ|<1

Lucky case: pq1.

                                                                ′
   Hölder’s inequality in ξn    ′         p′(∫    ∫            ′     ) pq′
(∗)        ≤         R−(p−1)|IR−1|1− q′            |gξn(ξ′)|q dξ′dξn
                                      IR−1 |ξ′|<1
R(p1)(R1)1pq=Rpq (1=1q+1q)

Unlucky case: pq>1. Hölder’s inequality goes in the wrong direction:

∫           (∫    )1
        1− 1s     s s
 Ah ≤ |A |     A h
for all s1, h0.

This is equality if h is constant on A.

PAUSE THIS.

Useful Harmonic Analysis tool

The locally constant property.

Convolution: Let f,gS(n). Define fgS(n) by

         ∫                 ∫
f ∗ g(x ) = ℝn f(x − y)g(y)dy = ℝn f(Y)g(x− y)dy.
See Young’s convolution:
∥f ∗g∥Lr ≤ ∥f∥p∥g∥q
when 1+1r=1p+1q.

Example. g=χB (B is the unit ball in n),

          ∫
f ∗ χB(x) = y∈B f(x− y)dy
RHS is “average value of f on B(x)”.

fχB is approximately constant on balls of radius 1”.

Support property: suppf=A, suppg=B, suppfgA+B={a+b:aA,bB}.

Convolution and Fourier Transform: fg^=f^g^.

Locally constant property: Let fS(n), suppf^B1(0). Then for any unit ball Bn and any xB,

            ∫
∥f∥L∞(B′) ≲m    |f|(x− y)----1-2-mdy.
              ℝn         (1+ |y|)

1(1+|y|2)m is 1 on |y|1.

PIC

Digesting f0.

For any unit interval In, xI,

          ∫
∥f ∥ ∞ ′ ≤        f(x− y)dy.
   L (I)   (− 12,12)

Suppose this:

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LHS has to be constant.

          ∫
∥f ∥L∞(I′) ≲ (    )f(x− y)dy.
            − 12,12

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        (∗)∫
∥f∥L∞(I′)≲        f (x − y)dy
            (− 12,12)

Lemma (Locally constant property). Assuming that:

  • fS(n)

  • suppf^B1(0)

Then for any unit ball B1n and any xB,
            ∫
∥f∥ ∞  ′ ≲      |f|(x− y)----1----dy.
   L (B ) m   ℝn         (1+ |y|2)m

The proof of this fact is more important than the statement – we will be using the strategy in future.

Proof of locally constant property. Let fS(n), suppf^B1(0). Let φCc(n) such that φ1 on B1(0), suppφB2(0).

By Fourier inversion:

    ˇ    ˇ      ˇ
f = (f^) = (^fφ) = (^f)∗ ˇφ = f ∗φˇ.
Let x0,xB1 (unit ball in n).

Math input error
Returning to RPn1(qp)RPn1,loc(qp)

∫        ′     ∫         ′
    |f (x)|pdx ≲    |fφBR |p dx
 BR             BR
where φBRS(n) satisfies φBR1 on BR, suppφBR^BR1(0).

̂fφBR =         ◟^f◝◜◞        ∗ φ◟^B◝R◜◞ (ξ)
       R−1 neighbourhood of 𝒫n−1 R−1 ball
supported in 2R1-neighbourhood of Pn1.

Repeat steps of proof:

BR|f(x)|pdxR(p1)IR1(|ξ|<1|hξn(ξ)|qdξ)pqdξn

PIC

       |------------------|
       |^       ′  ′2     |
h(ξn) =-f ∗-^φBR(ξ,|ξ|-+-ξn)

6 Tube incidence implications of Fourier restriction

Last time: LOCALLY CONSTANT PROPERTY (think of it more as “heuristic”).

If suppf^B1 then

What if suppf^Bλ(0) (so |f|const on λ1-balls)?

     ˇ^
f = (fφBλ) = f ∗ ˇφBλ,
where φBλ(ξ)=φB1(λ1ξ).
         ∫                       ∫
φˇB  (x ) =   e2πix⋅ξφB  (λ −1ξ)dξ = λn    e2πi(λx)⋅ξφB (ξ)dξ = λnφˇB (λx).
   λ      ℝn        1             ℝn          1            1
|ˇφB1| is approximately averaging over a 1-ball. |ˇφBλ| is approximately averaging over λ1-balls.

What about suppf^Bλ(v)?

Same thing happens, because eixsomethingf will have Fourier support in Bλ(0), and taking absolute values means we don’t notice the eixsomething (modulation).

Returning to RPn1(qp)RPn1,loc(qp).

∫        ′    ∫               ′
   |f(x)|p dx ≲    |f (x)φBR (x )|pdx
 BR            BR
φBR(x)S(n), |φBR|1 on BR, suppφBR^BR1(0).

Last lecture

     (                              )             (             ) p′
∫    (∫       ^       ′  ′2     q′  ′)  (∗)  −1 1− pq′′ ∫    ^   q′   q′
 I−1    |ξ′|<1  |f ∗ ^φBR (ξ ,|ξ |+ ξn)| dξ  ≲ (R  )      ξ∈ℝn |f(ξ)| dξ   .
 R     ξ′∈ℝn−1

Can choose φBR^ such that

Case 1: pq1. LHS of () (using Hölder) is

|IR1|1pq(IR1|ξ|<1(|f^||φBR^|1q+1q(ξ,|ξ|2+ξn))qdξdξn)pq|IR1|1pq(n(|f^||φBR^|(ξ))qddξ)pq(|f^|q(ξη)|φBR^|(η)dη)1qmunder(∫      q) 1q
    |φ^BR |q
◟----◝◜----◞ 1 (Holder) |IR1|1p q (nn|f^|q (ξ η)|φBR^|(η)dηdξ) p q |IR1|1p q (n|f^|q(ξ)dξ) p q

Case 2: pq>1. Use P12 for intuition.

PIC

Imagine a function g which is approximately constant on each R1 cube Q. Think of g as g=QgQ.

     (                 ) p′-
∫      ∫        2        q′
            g(t,t + ξ2)dt    dξ2.
 IR−1   |t|<1
Note
∫        2
 |t|<1g(t,t + ξ2)dt ∼ gQ.
Therefore,
∫
     g(t,t2 + ξ2)dt ∼ C
 |t|<1
if |ξ2|cR1. (C for all ξ2IR1).

|(P+(0,ξ2))Q|R1 for |ξ2|cR1.

|IR1|1pq+pqCpq=|IR1|1pq(|IR1|C)pq(R1|t|<1g(t,t2+ξ2)dtdξ2)pq

Important: locally constant property means we didn’t need pq<1, like before.

Make the intuition rigorous.

                                                         p′
                       (∫                           ′  ) q′-
LHS of (∗) ≲ |IR−1| max       (|^f|∗|^φBR|(ξ′,|ξ′|2 + ξn))qdξ′
                 ξ2∈IR−1  |ξ′|<1
Consider the integral:

|ξ|<1(|f^||φBR^|(ξ,|ξ|2+ξn))qdξ=|ξ|<1(n|f^|(η)|φBR^|((ξ,|ξ|2+ξn)η)dη)qdξ|ξ|<1(n|f^|q(η)|φBR^|((ξ,|ξ|2+ξn)η)dη)dξSame pointwise HolderRn(R1)n1n|f^|q(η)dηR1n|f^|q(R1)1Rpq(|f^|q)pq

˙

Index