Probability density expansion

In summary: A Gaussian wave packet will dissipate over time, but it can be a bit more work to calculate the dispersion parameter, or to calculate the eigenvalues and eigenvectors of the wave function.
  • #1
Killtech
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This is probably a stupid question but i don't want to make a stupid mistake here, so i thought better ask: I'm starting with the simple free Schrödinger Equation ##V(x)=0## (can be 1 dim) and an initial condition where the wave function is somehow constrained to be entirely localized around a compact set (let it be a sphere) ##S## around ##x=0## and let ##\Psi(x,0)=0## everywhere else. Just like a Gaussian wave package this wave function should disperse over time.

What's the easiest way to calculate ##\rho(d,t)=|\Psi(d,t)|^2## for an arbitrarily distant point ##d## outside ##S##?

I don't think i an can assume ##\Psi## to initially take the form of an indicator function since it's not differentiable around the edges and right now no other function with compact support comes into my mind that is easy to decompose into ##|p>## states for that matter. something like ##exp(\frac {1} {x^2-1})## in ##[-1; +1]## doesn't seem to be particularly friendly with Fourier transform.
 
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  • #2
Killtech said:
This is probably a stupid question but i don't want to make a stupid mistake here, so i thought better ask: I'm starting with the simple free Schrödinger Equation ##V(x)=0## (can be 1 dim) and an initial condition where the wave function is somehow constrained to be entirely localized around a compact set (let it be a sphere) ##S## around ##x=0## and let ##\Psi(x,0)=0## everywhere else. Just like a Gaussian wave package this wave function should disperse over time.

What's the easiest way to calculate ##\rho(d,t)=|\Psi(d,t)|^2## for an arbitrarily distant point ##d## outside ##S##?

I don't think i an can assume ##\Psi## to initially take the form of an indicator function since it's not differentiable around the edges and right now no other function with compact support comes into my mind that is easy to decompose into ##|p>## states for that matter. something like ##exp(\frac {1} {x^2-1})## in ##[-1; +1]## doesn't seem to be particularly friendly with Fourier transform.

If you have a free particle with an initial wave-function that is known and is zero outside ##[-1, 1]##, then technically you express that initial wave-function as a distribution of free-particle states and apply time evolution. In general, that will get messy I imagine.
 
  • #3
PeroK said:
If you have a free particle with an initial wave-function that is known and is zero outside ##[-1, 1]##, then technically you express that initial wave-function as a distribution of free-particle states and apply time evolution. In general, that will get messy I imagine.
that much i know, hence the "What's the easiest way" in my question ^^. Looking for a Fourier friendly initial function to have the least mess possible, especially when transforming the time developed thing back.
 
  • #4
Killtech said:
that much i know, hence the "What's the easiest way" in my question ^^. Looking for a Fourier friendly initial function to have the least mess possible, especially when transforming the time developed thing back.

The classical example is the initial Gaussian. There are not many, I suspect, that result in a closed-form solution.
 
  • #5
PeroK said:
The classical example is the initial Gaussian. There are not many, I suspect, that result in a closed-form solution.
I know, but the Gaussian does not have compact support but instead already fills the entirety of space right from the start. I could take the Fourier transform of an indicator function but not sure if that is a valid case given that its initial state violates Schrödinger by not being differentiable at the edges.
 
  • #6
Killtech said:
I know, but the Gaussian does not have compact support but instead already fills the entirety of space right from the start.

That's the price you pay for (relative) mathematical simplicity.
 

1. What is probability density expansion?

Probability density expansion is a mathematical technique used in statistics to approximate a probability density function (PDF) by expanding it into a series of simpler functions. This allows for easier analysis and calculation of probabilities in complex systems.

2. How does probability density expansion work?

Probability density expansion works by breaking down a complex PDF into a series of simpler functions, such as polynomials or trigonometric functions. These simpler functions are then combined using coefficients to approximate the original PDF.

3. What are the benefits of using probability density expansion?

One benefit of using probability density expansion is that it allows for easier analysis and calculation of probabilities in complex systems. It also helps to simplify and visualize complex data, making it easier to interpret and draw conclusions from.

4. What types of problems can be solved using probability density expansion?

Probability density expansion can be used to solve problems related to probability and statistics, such as calculating the likelihood of a certain outcome in a complex system or analyzing data sets with multiple variables.

5. Are there any limitations to using probability density expansion?

While probability density expansion can be a useful tool, it does have some limitations. It may not accurately represent the original PDF in some cases, and it may be difficult to determine the appropriate number of terms to include in the expansion. Additionally, it may not be suitable for highly non-linear or discontinuous functions.

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