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The Hilbert transform is the tempered convolution with pv(1/(pi x)) and has signum Fourier multiplier

Statement

Assume Countable Choice and use the e−2πixξ Fourier convention of Fourier transform of a tempered distribution. Define the tempered distribution W=pv⁡1πx by its pairing with a Schwartz test function, ⟨W,φ⟩ equal to

1π∫∣x∣>1φ(x)x dx+1π∫∣x∣<1φ(x)−φ(0)x dx.

Then, for every Schwartz function f:

  1. the principal value lim⁡ε↓0Hεf(x) of Truncated Hilbert transform and principal value exists at every x∈R, and equals (W∗f)(x) for the tempered convolution of Convolution of a tempered distribution with a schwartz function;
  2. hence Hf:=W∗f is a tempered distribution, and F(Hf)=−isgn⁡(ξ)f^(ξ), where sgn⁡(0)=0 and f^=Ff is the Schwartz transform of f.

The principal value is taken symmetrically about the singularity, and the statement is made for Schwartz functions only; no Lp mapping property and no almost-everywhere statement for general f is asserted.

Facts & Assumptions

Given: Countable Choice, the Schwartz space S(R) and its seminorms pαβ(φ)=sup⁡x∣xα∂βφ(x)∣, and the Fourier convention φ^(ξ)=∫φ(x)e−2πixξdx.

[F1]

The truncated Hilbert transform is Hεf(x)=1π∫∣t∣>εf(x−t)t dt, and the principal-value transform is its symmetric ε↓0 limit wherever it exists; the definition asserts no almost-everywhere existence by itself. Truncated Hilbert transform and principal value

[F2]

The sine integral satisfies ∫0∞sin⁡uudu=π2, its partial integrals obey ∣S(T)∣≤3 for all T≥0 and ∣S(T)∣≤T for 0≤T≤1, and ∫ABsin⁡uudu≤2A in absolute value for 1≤A<B. The sine integral under Countable Choice: uniform bounds and the value pi/2

[F3]

The Fourier transform of a tempered distribution is defined by ⟨Fu,φ⟩=⟨u,Fφ⟩; the pairing is bilinear with no conjugation. Fourier transform of a tempered distribution

[F4]

For u∈S′(Rn) and Schwartz φ one has F(u∗φ)=(Fu)(Fφ), the product being the product of a tempered distribution with a smooth polynomially bounded function. Fourier transform converts allowed tempered convolutions to products

[F5]

The tempered convolution is defined by (u∗φ)(x)=⟨uy,φ(x−y)⟩, a scalar function of x. Convolution of a tempered distribution with a schwartz function

[F6]

A tempered distribution is a continuous complex-linear functional on Schwartz space. Tempered distribution

[F7]

Schwartz seminorms pαβ(φ)=sup⁡x∣xα∂βφ(x)∣ are finite for φ∈S(R). Schwartz space and its seminorms

[F8]

If f∈S(Rn) then f∈Lp for every 1≤p≤∞, with norm bounded by a finite sum of Schwartz seminorms. Schwartz derivatives are integrable

[F9]

The Fourier transform is a topological automorphism of Schwartz space, so f^∈S for f∈S. Fourier transform is a topological automorphism of Schwartz space

[F10]

Mean value theorem: for differentiable φ, ∣φ(x)−φ(0)∣≤∥φ′∥∞∣x∣ on [−1,1]. The mean value theorem, as the case g(x)=x of Cauchy's: for f continuous on [a,b] with a<b and differentiable on (a,b) there is c∈(a,b) with f(b)−f(a)=f′(c)(b−a)

[F11]

Fubini for L^1 functions on a sigma-finite product. Fubini's theorem for L^1 functions on a sigma-finite product

[F12]

Dominated convergence. Dominated convergence

[F13]

Substitution for improper integrals, with orientation retained for decreasing parametrizations. Change of variable in an improper integral

Proof

technique · direct
1.1F6F7F10

For φ∈S(R) both integrals in the definition of W converge absolutely: on ∣x∣>1 the bound ∣φ(x)/x∣≤p20(φ)∣x∣−3 is integrable, and on ∣x∣<1 the bound ∣φ(x)−φ(0)∣/∣x∣≤p01(φ) from [F10] is integrable on a set of length two. Hence ∣⟨W,φ⟩∣≤2π(p20(φ)+p01(φ)) and W is a tempered distribution by [F6]. Moreover ∫ε<∣x∣<1φ(0)/x dx=0 by oddness of 1/x, so for 0<ε<1 the truncated pairing (1/π)∫∣x∣>εφ(x)/x dx equals the defining two-piece pairing with the local piece integrated over ε<∣x∣<1; consequently ⟨W,φ⟩=lim⁡ε↓01π∫∣x∣>εφ(x)/x dx.

2.1step 1.1F2F8F11F13

Fix φ∈S(R) and 0<ε<R. By [F11] applied on the product of the finite-measure annulus {ε<∣x∣<R} with R, using the integrable majorant ∣φ(ξ)∣/∣x∣ from [F8], Iε,R:=1π∫ε<∣x∣<Rφ^(x)x dx=1π∫Rφ(ξ)Λε,R(ξ) dξ with Λε,R(ξ):=∫ε<∣x∣<Re−2πixξx−1dx. Writing the exponential in cosine and sine, the cosine term is odd and integrates to zero, while [F13] with u=2πxξ gives Λε,R(ξ)=−2i∫εRsin⁡(2πxξ)xdx=−2isgn⁡(ξ)(S(2πR∣ξ∣)−S(2πε∣ξ∣)) for the partial sine integral S of [F2]; in particular ∣Λε,R(ξ)∣≤12.

2.2step 1.1F1F5F8F10F12

Fix f∈S(R) and x∈R. For 0<ε<1, [F1] gives Hεf(x)=1π∫∣t∣>εf(x−t)t dt; since ∫ε<∣t∣<1f(x)/t dt=0, this equals 1π∫ε<∣t∣<1f(x−t)−f(x)t dt+1π∫∣t∣>1f(x−t)t dt. The tail is absolutely convergent by [F8], and the first integral converges as ε↓0 by [F12], the integrand tending pointwise to f(x−t)−f(x)t and being dominated on (−1,1) by p01(f)=∥f′∥∞ thanks to [F10]. The resulting limit is exactly ⟨Wy,f(x−y)⟩=(W∗f)(x) by the defining formula of W in 1.1 and the convolution definition [F5]. Hence lim⁡ε↓0Hεf(x) exists at every x and equals (W∗f)(x).

3.1step 1.1step 2.1F2F3F12

By 1.1 and [F3], ⟨FW,φ⟩=⟨W,φ^⟩=lim⁡ε↓01π∫∣x∣>εφ^(x)/x dx. Holding ε fixed, [F2] gives Λε,R(ξ)→ρε(ξ):=−2isgn⁡(ξ)(π2−S(2πε∣ξ∣)) as R→∞, with ∣ρε(ξ)∣≤π+6. Thus [F12] against ∣φ∣ yields Iε,R→1π∫φρε as R→∞. Then S(2πε∣ξ∣)≤2πε∣ξ∣ for 2πε∣ξ∣≤1 by [F2] makes ρε(ξ)→−iπsgn⁡(ξ) pointwise as ε↓0, and a second application of [F12] gives 1π∫φρε→∫(−isgn⁡ξ)φ(ξ) dξ. Combining the two limits with the pairing identity gives ⟨FW,φ⟩=∫(−isgn⁡ξ)φ(ξ) dξ for every φ∈S, that is, FW=−isgn⁡(ξ) as tempered distributions.

4.1step 2.2step 3.1F3F4F9∎

By 3.1 and [F4] applied to the tempered distribution W and the Schwartz function f, F(W∗f)=(FW)(Ff)=−isgn⁡(ξ)f^(ξ), the product being that of the distribution −isgn⁡ with the Schwartz function f^∈S supplied by [F9] and [F3]. By 2.2 the same Hf=W∗f is the pointwise principal-value transform of f; thus the principal value defines the tempered convolution with pv⁡1πx and has the signum Fourier multiplier, as claimed.

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