Annihilation of shifted gaussian vs. nonshifted gaussian

In summary, the non-shifted ground state wave function yields the ground state eigenfunction multiplied up by a constant because it can be obtained from the shifted ground state wave function by applying the annihilation operator a, as shown in equations (11) and (12). This relation can be checked by simply applying the annihilation operator to the given ground state wave function. Additionally, the shifted ground state is a coherent state, as shown in equation (5).
  • #1
Chip
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Maybe a picture will help. http://chipreuben.com/annihilation-of-shifted-Gaussian.jpg
I get zero, both with the shifted and the non-shifted. Can anyone tell why the nonshifted should yield the ground state eigenfunction multiplied up by a constant? It seems the answer has to do with expanding in Fourier mode and then converting back to the distance domain...but I'm not sure.
 
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  • #2
The most simple thing is to just check the equation. You've given the ground-state wave function (a Gaussian) and then you can simply apply the annihilation operator and check the given relation. That shows that the "shifted ground state" is a coherent state.
 
  • #3
Chip said:
Maybe a picture will help. http://chipreuben.com/annihilation-of-shifted-Gaussian.jpg
I get zero, both with the shifted and the non-shifted. Can anyone tell why the nonshifted should yield the ground state eigenfunction multiplied up by a constant? It seems the answer has to do with expanding in Fourier mode and then converting back to the distance domain...but I'm not sure.

Okay, I’m going to work in units where [itex]\hbar = m = \omega = 1[/itex]. In this units, the momentum operator is simply
[tex]p = \frac{i}{\sqrt{2}} \ (a^{\dagger} - a) . \ \ \ \ \ \ \ \ \ \ \ \ \ (1)[/tex]
Now, consider the translation operator, [itex]T(\epsilon) = \exp(- i \epsilon \ p)[/itex], whose action on coordinate state is given by [tex]T(\epsilon) | x \rangle = | x + \epsilon \rangle , \ \ \ \ \ \ (2)[/tex]
and define a state [itex]|\Psi_{\epsilon} \rangle[/itex] by the action of [itex]T(\epsilon)[/itex] on the ground state [itex]|\Psi_{0}\rangle[/itex]:
[tex]
|\Psi_{\epsilon} \rangle = T(\epsilon) \ | \Psi_{0}\rangle . \ \ \ \ \ \ \ \ \ \ \ \ \ \ (3)
[/tex]
By multiplying (3) from the left with [itex]\langle x|[/itex] and inserting the unit operator [itex]\int d \bar{x} |\bar{x}\rangle \langle \bar{x}| =1[/itex], we transfer (3) to a coordinate representation, i.e., equation between wave functions
[tex]
\langle x | \Psi_{\epsilon}\rangle = \int d \bar{x} \ \langle x | T(\epsilon) | \bar{x} \rangle \langle \bar{x}| \Psi_{0}\rangle . \ \ \ \ (4)
[/tex]
Using (2), we obtain
[tex]
\Psi_{\epsilon}(x) = \int d \bar{x} \ \langle x | \bar{x} + \epsilon \rangle \ \Psi_{0}(\bar{x}) . \ \ \ \ (5)
[/tex]
Now, change the integration variable [itex]\bar{x} = y - \epsilon[/itex], and use [itex]\langle x | y \rangle = \delta (x - y)[/itex] to obtain
[tex]
\Psi_{\epsilon}(x) = \int dy \ \langle x | y \rangle \ \Psi_{0}(y - \epsilon) = \Psi_{0}(x - \epsilon) . \ \ (6)
[/tex]
So, [itex]\Psi_{\epsilon}(x)[/itex] is the shifted ground state wavefunction [itex]\Psi_{0}(x - \epsilon)[/itex].


Okay, now go back to (3) and let the operator [itex]a[/itex] acts on it
[tex]
a \ |\Psi_{\epsilon}\rangle = a \ e^{- i \epsilon \ p} | \Psi_{0} \rangle . \ \ \ \ \ \ (7)
[/tex]
Since, [itex]a \ |\Psi_{0}\rangle = 0[/itex], we can replace the right-hand-side of (7) by the following commutator
[tex]
a \ |\Psi_{\epsilon}\rangle = \left[a \ , \ e^{- i \epsilon \ p} \right] | \ \Psi_{0} \rangle . \ \ \ \ \ \ \ (8)
[/tex]
If you now expand the exponential and use the relation
[tex]
[a \ , -i \epsilon \ p ] = \frac{\epsilon}{\sqrt{2}}\ [a \ , a^{\dagger}] = \frac{\epsilon}{\sqrt{2}} ,
[/tex]
you get
[tex]
\left[ a \ , e^{- i \epsilon \ p} \right] = \frac{\epsilon}{\sqrt{2}} \ e^{- i \epsilon \ p} . \ \ \ \ \ \ \ \ \ \ \ \ \ \ (9)
[/tex]
Substituting (9) in (8), leads to
[tex]
a \ |\Psi_{\epsilon}\rangle = \frac{\epsilon}{\sqrt{2}} \ e^{- i \epsilon \ p} \ | \Psi_{0} \rangle = \frac{\epsilon}{\sqrt{2}} \ |\Psi_{\epsilon} \rangle . \ \ \ \ \ \ (10)
[/tex]
In wave function language, this becomes
[tex]
a \ \Psi_{\epsilon}(x) = \frac{\epsilon}{\sqrt{2}} \ \Psi_{\epsilon} (x) . \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (11)
[/tex]
And finally using (6), i.e., [itex]\Psi_{\epsilon}(x) = \Psi_{0}(x - \epsilon)[/itex], we obtain
[tex]
a \ \Psi_{0}( x - \epsilon ) = \frac{\epsilon}{\sqrt{2}} \ \Psi_{0}( x - \epsilon ) . \ \ \ \ \ \ \ \ \ \ (12)
[/tex]
Notice that setting [itex]\epsilon = 0[/itex] gives you back
[tex]
a \ \Psi_{0}(x) = 0 .
[/tex]
 
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Related to Annihilation of shifted gaussian vs. nonshifted gaussian

1. What is the difference between a shifted gaussian and a nonshifted gaussian?

A gaussian function is a type of probability distribution that is commonly used in statistics and data analysis. A nonshifted gaussian refers to a gaussian function that is centered around the origin, while a shifted gaussian is centered around a different point on the x-axis.

2. How does the shifting of a gaussian function affect its properties?

Shifting a gaussian function can affect various properties such as its mean, standard deviation, and skewness. It can also change the shape of the distribution and the location of its peak.

3. What is the significance of comparing the annihilation of shifted gaussian vs. nonshifted gaussian?

Comparing the annihilation of shifted gaussian vs. nonshifted gaussian can provide insights into the effects of shifting on the distribution and its properties. It can also help in understanding the relationship between different variables in a dataset.

4. Can a nonshifted gaussian be transformed into a shifted gaussian?

Yes, a nonshifted gaussian can be transformed into a shifted gaussian by adding a constant value to all data points in the distribution. This constant value represents the shift in the distribution.

5. What are some real-life applications of studying the annihilation of shifted gaussian vs. nonshifted gaussian?

The comparison of shifted and nonshifted gaussian distributions has various applications in fields such as finance, physics, and engineering. It is commonly used in analyzing stock market data, studying particle collisions, and optimizing signal processing systems.

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