nLab Dirac distribution

Redirected from "Dirac delta".
Contents

Contents

Idea

A Dirac distribution or Dirac δ\delta-distribution δ(p)\delta(p) is the distribution that is given by evaluating a function at a point pp.

It is closely related to Dirac measures, in the language of measure theory.

Properties

Proposition

The distributional derivative of the Heaviside distribution Θ∈𝒟′(ℝ)\Theta \in \mathcal{D}'(\mathbb{R}) is the delta distribution δ∈𝒟′(ℝ)\delta \in \mathcal{D}'(\mathbb{R}):

∂Θ=δ. \partial \Theta = \delta \,.
Proof

For b∈C c ∞(ℝ)b \in C^\infty_c(\mathbb{R}) any bump function we compute:

∫∂Θ(x)b(x)dx =−∫Θ(x)∂b(x)dx =−∫ 0 ∞∂b(x)dx =−(b(x)| x→∞−b(0)) =b(0) =∫δ(x)b(x)dx. \begin{aligned} \int \partial\Theta(x) b(x) \, d x & = - \int \Theta(x) \partial b(x)\, dx \\ & = - \int_0^\infty \partial b(x) d x \\ & = - \left( b(x)\vert_{x \to \infty} - b(0) \right) \\ & = b(0) \\ & = \int \delta(x) b(x) \, dx \,. \end{aligned}

Fourier transform

Example

(Fourier transform of the delta-distribution)

The Fourier transform (this def.) of the delta distribution, via this example, is the constant function on 1:

δ^(k) =∫x∈ℝ nδ(x)e −2πikxdx =1 \begin{aligned} \widehat {\delta}(k) & = \underset{x \in \mathbb{R}^n}{\int} \delta(x) e^{- 2\pi i k x} \, d x \\ & = 1 \end{aligned}

This implies by the Fourier inversion theorem (this prop.) that the delta distribution itself has equivalently, in generalized function-notation, the expression

(1)δ(x) =δ^^(−x) =∫ k∈ℝ ne 2πik⋅xdk \begin{aligned} \delta(x) & = \widehat{\widehat{\delta}}(-x) \\ & = \int_{k \in \mathbb{R}^n} e^{2 \pi i k \cdot x} \, d k \end{aligned}

in the sense that for every function with rapidly decreasing partial derivatives f∈𝒮(ℝ n)f \in \mathcal{S}(\mathbb{R}^n) we have

f(x) =∫y∈ℝ nf(y)δ(y−x)dvol(y) =∫y∈ℝ n∫k∈ℝ nf(y)e 2πik⋅(y−x)dvol(k)dvol(y) =∫k∈ℝ ne −2πik⋅x∫y∈ℝ nf(y)e 2πik⋅ydvol(y)⏟=(−1) nf^(−k)dvol(k) =f^^(−x) \begin{aligned} f(x) & = \underset{y \in \mathbb{R}^n}{\int} f(y) \delta(y-x) \, dvol(y) \\ & = \underset{y \in \mathbb{R}^n}{\int} \underset{k \in \mathbb{R}^n}{\int} f(y) e^{2 \pi i k \cdot (y-x)} \, dvol(k)\, dvol(y) \\ & = \underset{k \in \mathbb{R}^n}{\int} e^{- 2 \pi i k \cdot x} \underset{= (-1)^n\widehat{f}(-k)}{ \underbrace{ \underset{y \in \mathbb{R}^n}{\int} f(y) e^{2 \pi i k \cdot y} \, dvol(y) } } \, dvol(k) \\ & = \widehat{\widehat{f}}(-x) \end{aligned}

which is just the statement of the Fourier inversion theorem for smooth functions (this prop.).

Relation to point-supported distributions

It is clear that:

The delta distribution is a compactly supported distribution, and in fact a point-supported distribution.

Proposition

Every point-supported distribution uu with supp(u)={p}supp(u) = \{p\} is a finite sum of multiplies of derivatives of the delta distribution at that point:

u=∑α∈ℕ n|α|≤kc α∂ αδ(p) u = \underset{ {\alpha \in \mathbb{N}^n} \atop { {\vert \alpha\vert} \leq k } }{\sum} c^\alpha \partial_\alpha \delta(p)

where {c α∈ℝ} α\{c^\alpha \in \mathbb{R}\}_\alpha, and for k∈ℕk \in \mathbb{N} the order of uu.

(e.g. Hörmander 90, theorem 2.3.4)

References

  • Lars Hörmander, The analysis of linear partial differential operators, vol. I, Springer 1983, 1990

Last revised on February 12, 2025 at 22:34:50. See the history of this page for a list of all contributions to it.