Uncertainty values for non-Gaussian functions

In summary, the uncertainties for non-Gaussian wave packet functions can be calculated using the statistical variance formula (\Delta a)^2 = \langle A^2 \rangle - \langle A\rangle^2. For the given functions, the uncertainties \Delta x and \Delta p were found to be \Delta x = 2 \sqrt{a \pi} and \Delta p = \frac{a \hbar}{\sqrt{10}}. However, these values were chosen somewhat arbitrarily and there is no specific method for determining uncertainties for non-Gaussian functions.
  • #1
PhyPsy
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Uncertainty values for non-Gaussian wave packet functions

Homework Statement


[itex]\phi(k)= \left\{ \begin{array}{cc} \sqrt{\frac{3}{2a^3}}(a-|k|), & |k| \leq a \\ 0, & |k|>a \end{array} \right.[/itex]
[itex]\psi(x)= \frac{4}{x^2}sin^2 (\frac{ax}{2})[/itex]
Calculate the uncertainties [itex]\Delta x[/itex] and [itex]\Delta p[/itex] and check whether they satisfy the uncertainty principle.

Homework Equations


[itex]\Delta x\Delta p \geq h/2[/itex]

The Attempt at a Solution


The solution is worked out in the book, which is [itex]\Delta k=a[/itex] and [itex]\Delta x=\pi /a[/itex]. I understand that for a Gaussian distribution, you can use the standard deviation as [itex]\Delta x[/itex] and [itex]\Delta k[/itex], and this leads to the lowest limit of the uncertainty relation, [itex]h/2[/itex]. I don't see how I'm supposed to come up with [itex]\Delta x[/itex] and [itex]\Delta k[/itex] for non-Gaussian functions, though. The book seems to just pick [itex]\Delta k=a[/itex] and [itex]\Delta x=\pi /a[/itex] somewhat arbitrarily without explaining why these values were chosen. Could someone tell me if there is a method for figuring out what the uncertainties should be for non-Gaussian functions like this one?
 
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  • #2
If we are measuring the eigenvalue [itex]a[/itex] of an observable [itex]A[/itex], then we may characterize the uncertainty [itex]\Delta a[/itex] in [itex]a[/itex] using the statistical variance:

[tex](\Delta a)^2 = \langle A^2 \rangle - \langle A\rangle^2.[/tex]

This definition works for any state we use to compute the expectation values. In fact, this definition is the one used in the derivation of the generalized uncertainty principle.
 
  • #3
[itex]\langle A\rangle=0[/itex] for both functions, so [itex](\Delta a)^2= \int ^\infty _{-\infty}a^2 f(a) da[/itex]

[itex](\Delta k)^2= \int ^\infty _{-\infty} k^2 |\phi(k)|^2 dk = \frac{3}{2a^3} [\int ^0 _{-a} k^2 (a+k)^2 dk + \int ^a _0 k^2 (a-k)^2 dk][/itex]
[itex]=\frac{3}{2a^3} [(\frac{1}{3} a^2 k^3 + \frac{1}{2} a k^4 + \frac{1}{5} k^5)| ^0 _{-a} + (\frac{1}{3} a^2 k^3 - \frac{1}{2} a k^4 + \frac{1}{5} k^5)| ^a _0[/itex]
[itex]=\frac{3}{2a^3} \frac{2}{30} a^5 = \frac{a^2}{10}[/itex]
[itex]\Delta k = \frac{a}{\sqrt{10}}[/itex]
[itex]\Delta p = \frac{a \hbar}{\sqrt{10}}[/itex]

[itex](\Delta x)^2= \int ^\infty _{-\infty} x^2 |\psi(x)|^2 dx = 16 \int ^\infty _{-\infty} \sin ^4 (\frac{ax}{2}) dx / x^2[/itex]
[itex]=16[\frac{1}{2} a Si(ax) - \frac{1}{4} a Si(2ax) - \sin ^4 (\frac{ax}{2}) / x]|^\infty _{-\infty} = 16(\frac{a \pi}{2} - \frac{a \pi}{4}) = 4a \pi[/itex]
[itex]\Delta x = 2 \sqrt{a \pi}[/itex]

[itex]\Delta p \Delta x = \frac{2a \hbar \sqrt{a \pi}}{\sqrt{10}}[/itex]
So whether or not this is greater than [itex]\hbar / 2[/itex] depends on what a is. Did I do something wrong here?
 
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  • #4
PhyPsy said:
[itex]\langle A\rangle=0[/itex] for both functions, so [itex](\Delta a)^2= \int ^\infty _{-\infty}a^2 f(a) da[/itex]

[itex](\Delta k)^2= \int ^\infty _{-\infty} k^2 |\phi(k)|^2 dk = \frac{3}{2a^3} [\int ^0 _{-a} k^2 (a+k)^2 dk + \int ^a _0 k^2 (a-k)^2 dk][/itex]
[itex]=\frac{3}{2a^3} [(\frac{1}{3} a^2 k^3 + \frac{1}{2} a k^4 + \frac{1}{5} k^5)| ^0 _{-a} + (\frac{1}{3} a^2 k^3 - \frac{1}{2} a k^4 + \frac{1}{5} k^5)| ^a _0[/itex]
[itex]=\frac{3}{2a^3} \frac{2}{30} a^5 = \frac{a^2}{10}[/itex]
[itex]\Delta k = \frac{a}{\sqrt{10}}[/itex]
[itex]\Delta p = \frac{a \hbar}{\sqrt{10}}[/itex]

I agree with this, but we'd also want to calculate [itex]\Delta p[/itex] using [itex] \hat{x} = -i\hbar d/dp[/itex] in the momentum representation. Note that [itex]\psi(x)[/itex] is not the Fourier transform of [itex]\phi(k)[/itex], so we really have two problems to solve here.

[itex](\Delta x)^2= \int ^\infty _{-\infty} x^2 |\psi(x)|^2 dx = 16 \int ^\infty _{-\infty} \sin ^4 (\frac{ax}{2}) dx / x^2[/itex]
[itex]=16[\frac{1}{2} a Si(ax) - \frac{1}{4} a Si(2ax) - \sin ^4 (\frac{ax}{2}) / x]|^\infty _{-\infty} = 16(\frac{a \pi}{2} - \frac{a \pi}{4}) = 4a \pi[/itex]
[itex]\Delta x = 2 \sqrt{a \pi}[/itex]

[itex]\Delta p \Delta x = \frac{2a \hbar \sqrt{a \pi}}{\sqrt{10}}[/itex]
So whether or not this is greater than [itex]\hbar / 2[/itex] depends on what a is. Did I do something wrong here?

Again, this is a completely different wavefunction, so we can't use the uncertainty in momentum that we computed from the other one. Use [itex]\hat{p} = -i\hbar d/dx[/itex] to compute it.
 
  • #5
Actually, [itex]\psi (x)[/itex] is the Fourier transform of [itex]\phi (k)[/itex], or at least it is supposed to be...

[itex]\psi (x)= \frac{1}{\sqrt{2 \pi}} \int ^\infty _{-\infty} \phi(k) e^{ikx} dk[/itex]
[itex]=\frac{\sqrt{3}}{2 \sqrt{\pi a^3}} [\int ^0 _{-a} (a+k)e^{ikx} dk + \int ^a _0 (a-k)e^{ikx} dk][/itex]
[itex]=\frac{\sqrt{3}}{2 \sqrt{\pi a^3}} [\int ^0 _{-a} ke^{ikx} dk - \int ^a _0 ke^{ikx} dk + a \int ^a _{-a} e^{ikx} dk][/itex]
[itex]=\frac{\sqrt{3}}{2 \sqrt{\pi a^3}} [\frac{a}{ix} e^{-iax} + (1 - e^{-iax} )/ x^2 - \frac{a}{ix} e^{iax} - (e^{iax} - 1)/ x^2 + 2a \sin (ax) /x][/itex]

At this point, the book just says that after some calculations, the answer is [itex]\psi (x) = 4 \sin ^2 (\frac{ax}{2}) / x^2[/itex]. I don't know how they came up with that; the answer I got is [itex]\frac{\sqrt{3}}{x^2 \sqrt{\pi a^3}} [1 - \frac{ax}{2} \sin (ax) - \cos (ax)][/itex].
 

1. What is the difference between Gaussian and non-Gaussian functions?

Gaussian functions have a symmetric, bell-shaped curve and are characterized by their mean and standard deviation. Non-Gaussian functions, on the other hand, do not have this regular shape and can take on various forms.

2. How are uncertainty values calculated for non-Gaussian functions?

Uncertainty values for non-Gaussian functions can be calculated using methods such as Monte Carlo simulation or bootstrapping. These methods involve generating multiple samples from the function and analyzing the variability of the results.

3. Can uncertainty values be applied to all types of non-Gaussian functions?

Yes, uncertainty values can be applied to any type of non-Gaussian function, as long as the function can be defined and evaluated mathematically.

4. How do uncertainty values affect the interpretation of results from non-Gaussian functions?

Uncertainty values provide a measure of the reliability or accuracy of the results from non-Gaussian functions. Higher uncertainty values indicate a greater degree of variability in the results, which may impact the conclusions drawn from the function.

5. Are there any limitations to using uncertainty values for non-Gaussian functions?

Uncertainty values are based on assumptions and may not accurately reflect the true variability of the function. Additionally, the methods used to calculate uncertainty values may not be suitable for all types of non-Gaussian functions.

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