Fourier analysis of a Lorentzian/Cauchy/Breit–Wigner distribution

In summary, the conversation discusses the difficulty of performing a certain task analytically and the possibility of using approximations to make it easier. The idea of using the Cauchy distribution and Fourier analysis is mentioned, along with the suggestion of using the calculus of residues as an alternative to numerical integration.
  • #1
nf405
5
0
So I'm supposed to do this but is it just me or is it too hard to do this analytically? (I put it into wolfram online integrator and he couldn't do it) I don't need it very accurate so are there any approximations to this distribution that I could use to make it easier? Anyone have any ideas of a clever way of doing this?

see http://mathworld.wolfram.com/CauchyDistribution.html

for the Cauchy distribution and

http://en.wikipedia.org/wiki/Fourier_analysis

for Fourier analysis if you can't remember the formula

I've never done any numerical integration so if that's the only way I have to learn how to do that from scratch so any clever tricks I could use to avoid that would be appreciated.
 
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  • #2
I suggest using the calculus of residues.
 

1. What is a Lorentzian/Cauchy/Breit-Wigner distribution?

A Lorentzian/Cauchy/Breit-Wigner distribution is a type of probability distribution that is used to describe a wide range of phenomena in physics and mathematics. It is named after the French mathematician Hendrik Lorentz, the French mathematician Augustin-Louis Cauchy, and the American physicist Gregory Breit and the German physicist Eugene Wigner. These distributions are characterized by a peak at a central value with a long tail on either side.

2. What is Fourier analysis?

Fourier analysis is a mathematical technique used to decompose a complex function into simpler components. It involves representing a function as a sum of sine and cosine waves of different frequencies. This technique is widely used in physics and engineering to analyze periodic phenomena and to solve differential equations.

3. How is Fourier analysis used to analyze a Lorentzian/Cauchy/Breit-Wigner distribution?

A Lorentzian/Cauchy/Breit-Wigner distribution can be written as a sum of sine and cosine waves of different frequencies, which makes it amenable to Fourier analysis. By performing Fourier analysis on the distribution, we can determine the central value, width, and shape of the distribution. This allows us to understand the underlying physical or mathematical phenomenon that the distribution represents.

4. What are the applications of Fourier analysis of a Lorentzian/Cauchy/Breit-Wigner distribution?

Fourier analysis of these distributions has various applications in physics, chemistry, and engineering. For example, it is used to analyze the spectral lines in atomic and molecular spectra, to study the behavior of atoms and molecules in magnetic fields, and to determine the energy levels of particles in particle physics experiments. It is also used in signal processing and image analysis to analyze periodic signals and images.

5. What are the limitations of Fourier analysis of a Lorentzian/Cauchy/Breit-Wigner distribution?

One limitation of Fourier analysis of these distributions is that it assumes the function being analyzed is periodic, which may not always be the case in real-world applications. Additionally, the results of Fourier analysis may be affected by noise or other sources of error, which can make it challenging to accurately determine the parameters of the distribution. In some cases, alternative mathematical techniques may need to be used to analyze these distributions more accurately.

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