Time Evolution of Gaussian Wave Packet in Anisotropic Harmonic Oscillator

In summary, the conversation discusses a problem involving a particle described by a Gaussian wave packet in a 2D anisotropic harmonic oscillator potential. The initial wave function is given and the goal is to find the time evolution of the system by expanding it in the basis of eigenstates. However, there seems to be an issue with the integral involved in calculating the coefficients, as it is supposed to be zero but that would result in all coefficients being zero and the wave function being zero as well. Further investigation is needed to determine the correct solution.
  • #1
dingo_d
211
0

Homework Statement



So this kinda incorporates my last questions. I have a particle described by a Gaussian wave packet. And it moves in 2D anisotropic h.o potential with commensurate frequencies (1:2). I've solved the x part (I was messing around with the nasty integral, which in the end turned out to be easy, because I chose my wave function at t=0 with wrong constants).

And I turned to y part.

The initial wave function is:

[tex]\psi(y,0)=\left(\frac{m\omega_y}{\pi\hbar}\right)^{1/4}e^{-\frac{\lambda y^2}{2}}[/tex]

where:
[tex]\lambda=\frac{m\omega_y}{\hbar}[/tex].

I need to find out the time evolution of the system by expanding it in the basis of eigenstates of anisotropic harmonic oscillator (and since it's just the multiple of 1D harmonic osc. I just use the Y part here).

So:

since [tex]\psi(y,0)=\sum_{n_y=0}^\infty c_{n_y}(0)\psi_{n_y}(y,t)[/tex] and

[tex]c_{n_y}(0)=\int_{-\infty}^\infty \psi(y,0)Y_{n_y}^*(y)[/tex]

My [tex]c_{n_y}(0)[/tex] is (with variable change [tex]\sqrt{\lambda}y=\eta[/tex]):

[tex]c_{n_y}(0)=\frac{1}{\sqrt{\pi}}\frac{1}{\sqrt{2^{n_y}n_y!}}\int_{-\infty}^\infty e^{-\eta^2}H_{n_y}(\eta)d\eta[/tex]

Mathematica and sources on the internet says:[tex]\int_{-\infty}^\infty e^{-\eta^2}H_{n_y}(\eta)d\eta=-\frac{\sqrt{\pi}2^{n_y-1}\left[(n_y-2)_2F_1\left(1;\frac{1-n_y}{2};\frac{1}{2};1\right)+1\right]}{\Gamma\left(1-\frac{n_y}{2}\right)}[/tex]

And that is 0 :\

So what am I doing wrong?

The reasoning is that, because there is no offset in y direction of the Gaussian the time evolution will be that the function never changes with time (the probability density will be static).

I solved x part and I got that, as my time goes by, the x part (probability) is oscillating (depending on the initial displacement and other constants). And as I make initial displacement smaller it just stays still for t>0.

But the problem is, if my coefficient in t=0 is zero and coeff. at t is given by:

[tex]c_{n_y}(t)=c_{n_y}(0)e^{-\frac{\imath}{\hbar}E_{n_y}t}[/tex] all coefficients will be zero! and then my wave function given by:

[tex]\psi(y,t)=\sum_{n_y=0}^\infty c_{n_y}(t)Y_{n_y}(y)[/tex] will be zero :\

Or am I missing sth here?
 
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  • #2
Homework Equations \psi(y,0)=\left(\frac{m\omega_y}{\pi\hbar}\right)^{1/4}e^{-\frac{\lambda y^2}{2}}c_{n_y}(t)=c_{n_y}(0)e^{-\frac{\imath}{\hbar}E_{n_y}t}\int_{-\infty}^\infty e^{-\eta^2}H_{n_y}(\eta)d\eta=-\frac{\sqrt{\pi}2^{n_y-1}\left[(n_y-2)_2F_1\left(1;\frac{1-n_y}{2};\frac{1}{2};1\right)+1\right]}{\Gamma\left(1-\frac{n_y}{2}\right)}The Attempt at a SolutionI think I'm missing sth here. Because this integral is supposed to be zero, but then my time evolution coefficients will be zero, which doesn't make sense.
 

1. What is Gaussian time evolution?

Gaussian time evolution is a mathematical model used to describe the evolution of a system over time. It is based on the Gaussian distribution, also known as the normal distribution, which is a symmetric bell-shaped curve that is commonly used to represent probability distributions.

2. How is Gaussian time evolution used in science?

Gaussian time evolution is commonly used in various fields of science, such as physics, chemistry, and biology, to describe the behavior of systems over time. It can be used to model physical processes, chemical reactions, and biological systems, among others.

3. What are the assumptions of Gaussian time evolution?

The main assumption of Gaussian time evolution is that the system being studied follows a Gaussian distribution. This means that the system's behavior can be described by its mean and standard deviation, and that most of the data points will fall within a certain range of values around the mean.

4. What are the limitations of Gaussian time evolution?

One limitation of Gaussian time evolution is that it assumes a linear relationship between variables. This means that it may not accurately model systems with non-linear behavior. Additionally, it relies on the assumption that the system follows a Gaussian distribution, which may not always be the case.

5. How is Gaussian time evolution different from other time evolution models?

Gaussian time evolution differs from other time evolution models in that it specifically uses the Gaussian distribution to describe the behavior of the system over time. Other models may use different types of distributions or mathematical equations to describe the evolution of a system.

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