Fourier transform of multivalued functions

In summary, the conversation discusses the evaluation of a Fourier transform containing two branch points and two poles on the imaginary axis. The integrand can be rewritten and evaluated using the convolution theorem, but there is a discrepancy between the numerical and analytical solutions. The participants are seeking help and suggestions for solving the integral.
  • #1
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Dear all,

I have recently come across the following Fourier transform (FT):

[itex]

I=\int_{-\infty}^{\infty} dx \, e^{-\imath x t} \frac{(1-x^2)}{(1+x^2)^{3/2} (a^2+x^2)}.

[/itex]

The integrand contains two branch points on the imaginary axis, plus two poles also residing on the imaginary axis. The two simple poles can either be on or off the branch cuts depending on the value of the constant "a". Admittedly, I am experiencing some problems evaluating the above FT. The integrand admit a finite analytical result (I mean when evaluated without the exponential term), so I believe also its FT should exists. One of the strategies I have tried has been to rewrite the integral as:

[itex]

I=\int_{-\infty}^{\infty} dx \, \frac{e^{-\imath x t}}{(1+x^2)^{1/2}} \frac{(1-x^2)}{(1+x^2) (a^2+x^2)}.

[/itex]

and use the convolution theorem for the "two" functions. The FT of the inverse square root function is the modified Bessel function of the First kind, while the second one can be easily evaluated since it only contains simple poles. I have performed the convolution using Mathematica, that after some time gave me a finite and nice looking answer. Unfortunately, by confronting the numerical and the analytical solution graphically, the two plots look slightly different, even though they show a similar behaviour... clearly something is missing.

Has everyone came across something similar? Do you have any suggestions on strategies to adopt in order to solve the above integral?

Thank you!
 
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  • #2
Trust the analytical solution, check the numerical solution.
If you find no clue, explain more here.
 

Related to Fourier transform of multivalued functions

1. What is a Fourier transform of multivalued functions?

A Fourier transform of multivalued functions is a mathematical operation that converts a function of time or space into a function of frequency. It is commonly used in signal processing and data analysis to decompose a complex signal into its component frequencies.

2. How does a Fourier transform of multivalued functions work?

The Fourier transform of a multivalued function works by breaking down the function into a series of sinusoidal functions with different frequencies, amplitudes, and phases. These components can then be combined to reconstruct the original function.

3. What is the difference between a Fourier transform and a Fourier series?

A Fourier transform of multivalued functions is a continuous representation of a function in the frequency domain, while a Fourier series is a discrete representation of a periodic function in the time or space domain. In other words, a Fourier transform is used for non-periodic signals, while a Fourier series is used for periodic signals.

4. What are the applications of Fourier transform of multivalued functions?

The Fourier transform of multivalued functions has various applications in fields such as engineering, physics, and mathematics. It is commonly used in signal and image processing, data compression, and solving differential equations. It also has applications in areas such as optics, acoustics, and quantum mechanics.

5. Are there any limitations to using Fourier transform of multivalued functions?

One limitation of Fourier transform of multivalued functions is that it assumes the function is continuous and has a finite energy. If the function is not continuous or has infinite energy, the Fourier transform may not accurately represent the original function. Additionally, care must be taken when dealing with functions that have sharp discontinuities or are non-differentiable.

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