Fourier transform of distributions.

In summary, the conversation discusses the possibility of calculating the Fourier transform of two functions, namely the derivative of the prime counting function and the derivative of the Tchebycheff function. The speaker believes that these integrals exist in either the Cauchy P.V or Hadamard finite part sense, but needs help in finding a method to give a finite value for every frequency. The conversation also mentions the challenges of using the definitions and the possibility of a divergent sum in the Fourier transform.
  • #1
Klaus_Hoffmann
86
1
Is there any way to calculate the Fourier transform of the functions

[tex] \frac{d\pi}{dx}-1/log(x) [/tex] and [tex] \frac{d\Psi}{dx}-1 [/tex]

(both are understood in the sense of distributions)

i believe that these integrals (even with singularities) exist either in Cauchy P.V or Hadamard finite part sense but if possble i would need a help, thanks

EDIT:= 'pi(x)' here is the prime counting function and 'Psi (x) ' is the Tchebycheff function.
 
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  • #2
what problems have you encountered just using the definitions?
 
  • #3
The problem mathwork is that as you can see the integral is 'sngular' i was looking for a method to give a FINITE value for every frequency, for example using the Cauchy P.V however i think this method (Cacuhy's) to extract a finite value does not work.. perhaps Hadamrd finite part ??, but i don't know how to apply it
 
  • #4
The derivative of the pi function, in the distributional sense, will be an infinite series of delta functions, as there are an infinite number of primes.
[tex]\frac{d\pi}{dx}= \delta(x-2) + \delta(x-3) + \delta(x-5) + \cdots[/tex]
So its Fourier transform for a particular frequency would be
[tex]\hat{f}(\omega)=e^{-i 2 \omega }+e^{-i 3 \omega }+e^{-i 5 \omega }+ \cdots [/tex]

I feel pretty sure that this would be a divergent sum, as the pi function is not L2 integrable itself. It seems highly improbable that the sum would converge for all frequencies.
 

1. What is a Fourier transform of distributions?

A Fourier transform of distributions is a mathematical operation used to convert a function or distribution from its original domain (usually time or space) to a representation in the frequency domain. It is a powerful tool in signal processing and can be used to analyze and manipulate complex signals and systems.

2. How is a Fourier transform of distributions different from a regular Fourier transform?

A regular Fourier transform is defined for functions that are square-integrable, whereas a Fourier transform of distributions can be applied to a wider range of functions, including non-square-integrable functions and distributions. It also allows for the representation of signals with infinite energy, such as impulse functions.

3. What are some applications of Fourier transform of distributions?

Fourier transform of distributions has many applications in various fields, including signal processing, image processing, quantum mechanics, and engineering. It is used to analyze and filter signals, solve differential equations, and study the properties of physical systems.

4. Can a Fourier transform of distributions be inverted?

Yes, a Fourier transform of distributions can be inverted using the inverse Fourier transform. This allows for the reconstruction of the original function or distribution in its original domain.

5. Is there a relationship between Fourier transform of distributions and Laplace transform?

Yes, the Fourier transform of a distribution is a special case of the Laplace transform, where the complex variable s is set to zero. The Laplace transform can be used to extend the Fourier transform of distributions to a larger class of functions and distributions.

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