Complex gaussian, complete the square

In summary, the conversation involves discussing the evaluation of an integral that is essentially the Fourier transform of a Gaussian function. The approach of completing the square is discussed and justified using Cauchy's theorem or the principle of analytic continuation. The conversation also touches on the need for rigor in taking the limit of the integral and applying Cauchy's theorem.
  • #1
Irid
207
1

Homework Statement


Evaluate this integral (in essence the Fourier transform of the Gaussian):

[tex] \int_{-\infty}^{+\infty} e^{-ax^2} e^{ikx}\, dx [/tex]

2. The attempt at a solution
One way is to complete the square, so that

[tex] -ax^2 + ikx = -a(x-ik/2a)^2 - (k/2a)^2[/tex]

so the integral becomes
[tex] e^{-(k/2a)^2} \int_{-\infty}^{+\infty} e^{-a(x-ik/2a)^2}\, dx [/tex]

then change the variable [tex] x \rightarrow x-ik/2a [/tex], so that the integral becomes like the usual Gaussian and the result is [tex] \sqrt{\pi/a} e^{-(k/2a)^2} [/tex].

3. Nuissance
The problem is that I have changed a real variable x into a complex one, while still keeping the same integration limits, which is plus minus infinity (on the real axis, not the complex plane). I don't see any legal reason behind this. What is more, if we use Euler's formula [tex] e^{ikx} = \cos(kx) + i\sin (kx) [/tex] and integrate the real and imaginary parts separately, the imaginary part vanishes because it's odd with respect to the origin, while cosine in the real part can be expanded as a Taylor series, each term then can be integrated with the Gaussian, we sum back the results and the final formula is exactly the same as with the supposedly illegal completion of the square, so evidently we can complete the square, with a good justification. What is the justification?
 
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  • #2
You either have to use Cauchy's theorem that says that the contour integral of an analytic function is zero to move the integration path back on the real axis, or you can use the principle of analytic continuation to analytically continue the real Gaussian integral to the complex case.
 
  • #3
OK, I see that Cauchy's integral theorem can do the trick. Unfortunately I don't have sufficient knowledge of complex analysis, but still I would like to have an elegant solution, as it is part of a bigger problem.
As far as I can understand, I define a contour on a complex plane, then the total integral is zero due to Cauchy, the real line displaced by -ik/2a is my integral, then I also know the Gaussian integral on the real axis, and finally I have two segments at plus and minus real infinity connecting these two lines. How can I show that they cancel?
 
  • #4
Irid said:
OK, I see that Cauchy's integral theorem can do the trick. Unfortunately I don't have sufficient knowledge of complex analysis, but still I would like to have an elegant solution, as it is part of a bigger problem.
As far as I can understand, I define a contour on a complex plane, then the total integral is zero due to Cauchy, the real line displaced by -ik/2a is my integral, then I also know the Gaussian integral on the real axis, and finally I have two segments at plus and minus real infinity connecting these two lines. How can I show that they cancel?

If you are rigorous, then the integral from minus to plus infinity first has to be written as the integral from minus R to plus R and then you take the limit of R to infinity. You apply Cauchy's theorem on the integrals inside the limits. What you then see is that the contributions of the two line segments from ±R to ±R - ik/(2a) tend to zero in the limit of R to infinity.
 

1. What is a complex Gaussian distribution?

A complex Gaussian distribution is a probability distribution that describes the likelihood of a complex-valued random variable taking on different values. It is defined by two parameters: the mean, which is a complex number, and the variance, which is a positive real number.

2. How is the complex Gaussian distribution related to the standard normal distribution?

The complex Gaussian distribution is closely related to the standard normal distribution in that it can be obtained by taking the real and imaginary parts of a complex normal random variable. This means that the real and imaginary parts of a complex Gaussian distribution follow independent normal distributions with the same mean and variance as the complex Gaussian distribution.

3. What is meant by "completing the square" in the context of the complex Gaussian distribution?

Completing the square is a mathematical technique used to simplify quadratic expressions. In the context of the complex Gaussian distribution, completing the square involves rewriting the expression for the probability density function in a way that makes it easier to integrate and calculate probabilities.

4. How is "completing the square" used to calculate probabilities for the complex Gaussian distribution?

Completing the square is used to rewrite the expression for the complex Gaussian probability density function in a way that allows us to use the standard normal distribution to calculate probabilities. This is done by factoring out the complex mean from the quadratic term and completing the square for the remaining term.

5. What are some applications of the complex Gaussian distribution in science?

The complex Gaussian distribution has many applications in science, including in signal processing, communication systems, and quantum mechanics. It is also commonly used in statistics and machine learning to model complex-valued data. Additionally, the complex Gaussian distribution is widely used in physics and engineering to describe the behavior of systems with random complex-valued inputs or outputs.

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