Fourier transform on a distribution

In summary: -\infty}^{\infty} \frac {x^{3n+2}}{(2n)!} e^{-2\pi i yx} dx\right) dy= i\sum_{n=0}^{\infty} \int_{-\infty}^{\infty} \hat{\varphi}(y) \hat{\delta}(3n+2-y) dy= i\sum_{n=0}^{\infty} \hat{\varphi}(3n+2)= i\hat{\varphi}(2) + i\hat{\varphi}(5) + i\hat{\varphi}(8) + ...= i\sum_{n=
  • #1
jerry109
2
0

Homework Statement



Determine the Fourier transform on the tempered distribution:

[tex]
\langle f, \varphi \rangle
[/tex]

Where [tex] f[/tex] can be given by they taylor series representation:

[tex]
f = i\sum_{n=0}^{\infty} \frac {x^{3n+2}}{(2n)!}
[/tex]

The Attempt at a Solution



Fourier transform on tempered distribution is:

[tex]
F\langle f, \varphi \rangle = \langle F f, \varphi \rangle = \langle f, F \varphi \rangle

= i\int \sum_{n=0}^{\infty} \frac {x^{3n+2}}{(2n)!} \hat{\varphi} dx

= i\sum_{n=0}^{\infty}\int \frac {x^{3n+2}}{(2n)!} \hat{\varphi} dx
[/tex]

I'm stuck as how to resolve the infinite sum involving the Fourier transform of the test function [tex]\varphi[/tex].

Perhaps:

[tex]
i\sum_{n=0}^{\infty}\int \frac {x^{3n+2}}{(2n)!} \hat{\varphi} dx = i\sum_{n=0}^{\infty}\int \frac {x^{3n+2}}{(2n)!} \hat{\delta}(x-x_0) dx
= i\sum_{n=0}^{\infty}\int \frac {x^{3n+2}}{(2n)!} e^{2{\pi}ikx_0}dx= ie^{2{\pi}ik{x_0}}\sum_{n=0}^{\infty}\int \frac {x^{3n+2}}{(2n)!} dx

= ie^{2{\pi}ik{x_0}}\sum_{n=0}^{\infty}\frac {x^{3n+3}}{(2n)! * (3n+3)!}

[/tex]Any help would be fantastically appreciated,
Jerry109
 
Last edited:
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  • #2
Dear Jerry109,

Thank you for sharing your attempt at solving this problem. I can see that you are on the right track, but there are a few things that need to be clarified.

Firstly, when considering the Fourier transform on tempered distributions, it is important to remember that it is defined as an integral, not a sum. So instead of using a summation notation, we should be using an integral notation. Also, the tempered distribution f is not given by a Taylor series representation, but rather by an integral representation. So we should be using an integral representation for f as well.

With that being said, here is how I would approach this problem:

We know that the Fourier transform of a tempered distribution f is given by:

F\langle f, \varphi \rangle = \langle F f, \varphi \rangle = \langle f, F \varphi \rangle

= \int f(x) \hat{\varphi}(x) dx

= \int i\sum_{n=0}^{\infty} \frac {x^{3n+2}}{(2n)!} \hat{\varphi}(x) dxNow, we need to use the definition of the Fourier transform for a function g(x) given by:

\hat{g}(k) = \int_{-\infty}^{\infty} g(x) e^{-2\pi i kx} dx

So, substituting g(x) = i\sum_{n=0}^{\infty} \frac {x^{3n+2}}{(2n)!} \hat{\varphi}(x) in the above equation, we get:

F\langle f, \varphi \rangle = \int i\sum_{n=0}^{\infty} \frac {x^{3n+2}}{(2n)!} \hat{\varphi}(x) dx

= \int i\sum_{n=0}^{\infty} \frac {x^{3n+2}}{(2n)!} \int_{-\infty}^{\infty} \hat{\varphi}(y) e^{-2\pi i yx} dy dx

= i\sum_{n=0}^{\infty} \int_{-\infty}^{\infty} \hat{\varphi}(y) \left(\int_{
 

Related to Fourier transform on a distribution

1. What is a Fourier transform on a distribution?

A Fourier transform on a distribution is a mathematical operation that converts a function from its original domain (e.g. time or space) to its corresponding representation in the frequency domain. It is commonly used in signal processing and allows for the analysis of a signal's frequency components.

2. How is a Fourier transform on a distribution different from a regular Fourier transform?

A regular Fourier transform can only be applied to functions that are square integrable, while a Fourier transform on a distribution can be applied to a wider class of functions known as distributions or generalized functions. This allows for the analysis of signals that are not necessarily continuous or differentiable.

3. What is the purpose of using a Fourier transform on a distribution?

The main purpose of using a Fourier transform on a distribution is to analyze the frequency components of a signal. It allows for the identification of dominant frequencies and the filtering of unwanted frequencies. It is also used in solving differential equations and in the study of partial differential equations.

4. Can a Fourier transform on a distribution be inverted?

Yes, a Fourier transform on a distribution can be inverted using the inverse Fourier transform on a distribution. This allows for the reconstruction of the original signal in the time or space domain from its representation in the frequency domain.

5. Is there a relationship between the Fourier transform on a distribution and the Laplace transform?

Yes, there is a relationship between the Fourier transform on a distribution and the Laplace transform. The Fourier transform on a distribution can be seen as a special case of the Laplace transform where the imaginary part of the Laplace transform variable is equal to zero. This means that the Fourier transform on a distribution is a special case of the Laplace transform that is only applied to functions defined on the real line.

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