Proving the Limit of Dirac Delta from Normal Distribution

In summary, to show that the Dirac delta is the limit of the normal distribution, one must prove that the limit satisfies certain properties and that the integral of the limit is equal to the limit of the integral. This can be done by using a test function and showing that it works for any infinitely differentiable function.
  • #1
ehrenfest
2,020
1

Homework Statement


How would one show that dirac delta is the limit of the normal distribution?
http://en.wikipedia.org/wiki/Dirac_delta
using the definition [tex] \delta(k) = 1/(2\pi)\int_{-\infty}^{\infty}e^{ikx}dx [/tex]

Homework Equations


The Attempt at a Solution

 
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  • #2
Equality for distributions is defined pointwise. You just have to prove you get the same value if you convolve either one with a test function. I.E. for any test function f, you have to prove

[tex]
\int_{-\infty}^{+\infty} \delta(k) f(k) \, dk
=
\frac{1}{2\pi} \int_{-\infty}^{+\infty} \int_{-\infty}^{+\infty}
e^{ikx} f(k) \, dk \, dx
[/tex]
 
  • #3
One way (not so rigorous mathematically) to define the delta function is that it is a function that satisfies [tex]\delta(x)=0[/tex] if [tex]x\ne 0[/tex], and [tex]\textstyle \int_{-\infty}^{+\infty}dx\;\delta(x)=1.[/tex] So you need to show (1) that the limit of the normal distribution has these properties, and (2) that [tex]\textstyle{1\over2\pi}\int_{-\infty}^{+\infty}dk\;e^{ikx}[/tex] has these properties. Part (1) is easy. Amusingly, the easiest way to do part (2) is to define it by inserting a convergence factor of [tex]\exp(-\epsilon^2 k^2/2)[/tex] into the integrand, which turns it into a normal distribution that becomes a delta function in the limit [tex]\epsilon\to 0.[/tex]
 
  • #4
Oh, hah, I misread the problem. I thought the equation the OP posted was the equation he wanted to prove.

The idea is the same, though. For a distribution F(_) and a family of distributions G(_, y), to prove

[tex]
F(x) = \lim_{y \rightarrow 0} G(x, y)
[/tex]

you have to show

[tex]
\int F(x) f(x) \, dx = \lim_{y \rightarrow 0} \int G(x, y) f(x) \, dx
[/tex]
 
  • #5
Can I choose any f(x)?
 
  • #6
It has to be infinitely differentiable, or something like that (not up on my rigorous defs, sorry), but otherwise yes.
 

1. What is the Dirac Delta function?

The Dirac Delta function, also known as the unit impulse function, is a mathematical function that is defined as zero everywhere except at the origin, where it is infinite, with the total area under the curve equal to one. It is commonly used in mathematics and physics to represent a point source or a distribution of point masses.

2. How is the Dirac Delta function related to the Normal Distribution?

The Dirac Delta function is closely related to the Normal Distribution, also known as the Gaussian Distribution. In fact, the Dirac Delta function can be thought of as a limiting case of the Normal Distribution, where the standard deviation approaches zero and the area under the curve becomes infinitesimally small at the origin.

3. How is the limit of Dirac Delta from Normal Distribution proven?

The limit of Dirac Delta from Normal Distribution can be proven using the concept of the Dirac Delta function as a limiting case of the Normal Distribution. By taking the limit of the Normal Distribution as the standard deviation approaches zero, we can show that the resulting function is the Dirac Delta function.

4. Why is proving the limit of Dirac Delta from Normal Distribution important?

Proving the limit of Dirac Delta from Normal Distribution is important because it provides a rigorous mathematical basis for the use of the Dirac Delta function in various fields of science and engineering. It allows us to use the Dirac Delta function as a convenient mathematical tool for representing point sources or point masses in physical systems.

5. Are there any applications of the Dirac Delta function in real-world scenarios?

Yes, the Dirac Delta function has many applications in real-world scenarios. It is commonly used in physics to represent point charges, in engineering to model point loads, and in signal processing to analyze signals with a continuous spectrum. It also has applications in fields such as quantum mechanics, fluid dynamics, and image processing.

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