Probability of Staying in x<0 for Superposition of 2 Gaussians

In summary, the probability current is the probability of finding the particle in a certain region, and is calculated by plugging the wavefunction into the Current equation.
  • #1
WWCY
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Homework Statement


I am supposed to find probability of staying in x < 0 for a superposition of two Gaussians. The wavefunction is something along the lines of:

Screen Shot 2017-11-05 at 5.43.08 PM.png


Homework Equations

The Attempt at a Solution


Usually, the step involved in finding probabilities for 1 particle is just to perform the integral of ##|\Psi|^2## between 2 points (##-\infty## to 0 i believe) . However, I believe this wavefunction is the sum of 2 separately normalized gaussians. I'm not sure how I should proceed.

Advice is greatly appreciated!
 

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  • #2
You can still calculate this integral. Just calculate the integral of both separately and add them.
 
  • #3
mfb said:
You can still calculate this integral. Just calculate the integral of both separately and add them.

Thanks for the response!

But if I choose to do both integrals from -infinity to infinity, and sum them, does that not give me a probability of 2?
 
  • #4
WWCY said:

Homework Statement


I am supposed to find probability of staying in x < 0 for a superposition of two Gaussians. The wavefunction is something along the lines of:

View attachment 214386

Homework Equations

The Attempt at a Solution


Usually, the step involved in finding probabilities for 1 particle is just to perform the integral of ##|\Psi|^2## between 2 points (##-\infty## to 0 i believe) . However, I believe this wavefunction is the sum of 2 separately normalized gaussians. I'm not sure how I should proceed.

Advice is greatly appreciated!

The probability that ##x < 0## at time ##t## is ##\Pr(x < 0|t) = P(t) = \int_{-\infty}^0 |\psi(x,t)|^2 \, dx##, so you need to write out the four terms of ##|\psi|^2 = \psi^* \, \psi## and then integrate them separately. It will be complicated and unpleasant.

However, I am not quite sure what the statement "staying in ##x < 0##" means. Interpreted literally, it is asking for the probability that ##x<0## for all ##t##, and this is very different from asking that ##x < 0## for any single value of ##t##. Is that really what is wanted?
 
  • #5
Ray Vickson said:
The probability that ##x < 0## at time ##t## is ##\Pr(x < 0|t) = P(t) = \int_{-\infty}^0 |\psi(x,t)|^2 \, dx##, so you need to write out the four terms of ##|\psi|^2 = \psi^* \, \psi## and then integrate them separately. It will be complicated and unpleasant.

Will this not give me something unphysical? In the problem, these Gaussians were normalized separately, with A being their normalization constants, if I chose to do the integral from ##-\infty## to ##\infty##, would it not give me something > 1?

Ray Vickson said:
However, I am not quite sure what the statement "staying in ##x < 0##" means. Interpreted literally, it is asking for the probability that ##x<0## for all ##t##, and this is very different from asking that ##x < 0## for any single value of ##t##. Is that really what is wanted?

Yep, I was asked to investigate how the probability for the region ##-\infty## to 0 evolved with time, so I do need an expression for all ##t##. To provide some context, all of this is part of a project about quantum backflow, and I'm tasked to do the "simpler" calculations like the ones above.

Thanks for the assistance!
 
  • #6
Ray Vickson said:
staying in x<0" means. Interpreted literally, it is asking for the probability that x<0 for all t
Quite. I wonder if it was intended to ask for the probability at t=∞.
WWCY said:
these Gaussians were normalized separately
Then you do not have enough information. You need to know the proportions in which to combine them before renormalising.
 
  • #7
If the given wavefunction is properly normalized, then the normalization constants take care that the total integral over the squared wave function is 1. This means ##|A_1|^2 + |A_2|^2 = 1##. An integral over a smaller x range will then give a value smaller than 1.

If the question asks for ##t \to \infty##, you'll need a different approach. Hint: Look at the momentum distribution.
 
  • #8
Hi all, thanks for the responses, I think it's best for me to upload the paper I'm referring to.

https://arxiv.org/pdf/1301.4893.pdf

All of the stuff that I'm referring to is on pages 4 and 5 of the document. In particular, I'm looking to recover analytic expressions for Figures 1. (Probability Current) and 2. (Probability from ##-\infty<x<0##).

For probability current, I'm assuming that it's just a case of plugging the wavefunction into the Current equation.

I'd really appreciate it if someone could look through it and advise me on the interpretation!
 
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  • #9
haruspex said:
Then you do not have enough information. You need to know the proportions in which to combine them before renormalising.

Do you mean something like ##\frac{1}{\sqrt{2}} \psi_1 + \frac{1}{\sqrt{2}} \psi_2##?
 
  • #10
I initially made a post in the HW section about this topic but I believe that my question was poorly formulated, hence I'm attempting to ask a better one here, since it isn't exactly HW.

I am studying the probability current, and probability of a sum of 2 gaussians. The type of wavefunction is as shown :
Screen Shot 2017-11-05 at 5.43.08 PM.png

where ##\hbar = m = 1##

The ultimate goals are to:
a) Obtain an analytic expression for Probability Current at x = 0
b) Obtain an analytic expression for probability from ##-\infty < x < 0## for all ##t## - to study how the probability of the two wavepackets remaining at ##x<0## changes with time.
c) To obtain plots similar to Figures 1 (Probability Current) and 2 (Probability) in this paper (pages 4 and 5 respectively): https://arxiv.org/pdf/1301.4893.pdf

What I intend to do:

I was given an initial Gaussian of: ##\Psi(x,0) = e^{ipx}e^{\frac{-x^2}{2\sigma ^2}}##.

Normalizing, finding ##\widetilde{\psi}## and then ##\Psi(x,t)## for 1 Gaussian gives,
$$\Psi_k = \frac{\sqrt{\sigma}}{\pi^{1/4} \sqrt{\sigma ^2 + it}}exp(ip_k (x - \frac{p_k}{2}t) + \frac{(x - p_k t)^2}{2(\sigma ^2 + it)}) $$
then summing over 2 Gaussians will give,
$$\Psi_k =\sum_{k=1,2} \frac{\sqrt{\sigma}}{\pi^{1/4} \sqrt{\sigma ^2 + it}}exp(ip_k (x - \frac{p_k}{2}t) + \frac{(x - p_k t)^2}{2(\sigma ^2 + it)}) $$
which is not dissimilar to the one above, except for the fact that I should have started with ##\Psi = e^{ipx}e^{\frac{-x^2}{4\sigma ^2}}##, I believe.

For a: I intend to place the Sum of Gaussians into the Probability Current equation, and chug through the algebra to derive the expression, evaluating the expression at x = 0.

For b: This is the part where I am not too sure about what to do. This is a sum of separately normalized wavefunctions, simply performing ##\int_{-\infty}^{0}|\Psi(x,t)|^2 dx## does not seem the right thing to do as I believe such functions don't seem to be orthogonal, and more fundamentally ##\int_{-\infty}^{\infty}|\Psi(x,t)|^2 dx## might ##>1## (?).

In my earlier post, some commenters seemed to question my interpretation of a) and b). I too am slightly unsure and I hope someone can take the time to skim through the noted sections of the paper and provide me with some advice, or some notes I should go through before attempting to solve the problem.

Any assistance is greatly appreciated!
 

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  • #11
Why do you feel each Gaussian must have equal weighting? The coefficients for each term could quite general as long as the whole this is normalised.
 

Related to Probability of Staying in x<0 for Superposition of 2 Gaussians

1. What is the probability of staying in x<0 for a superposition of 2 Gaussians?

The probability of staying in x<0 for a superposition of 2 Gaussians depends on the specific parameters of the Gaussians, such as their means and standard deviations. Generally, the closer the means of the Gaussians are to each other, the higher the probability of staying in x<0. The standard deviations also play a role, with smaller standard deviations resulting in a higher probability of staying in x<0.

2. How is the probability of staying in x<0 affected by the number of Gaussians?

The probability of staying in x<0 for a superposition of Gaussians increases as the number of Gaussians increases. This is because the superposition of multiple Gaussians can lead to a narrower and taller distribution, making it more likely for the values to fall within x<0.

3. Can the probability of staying in x<0 be calculated analytically for a superposition of 2 Gaussians?

Yes, the probability of staying in x<0 for a superposition of 2 Gaussians can be calculated analytically using the cumulative distribution function (CDF) of the normal distribution. By finding the area under the curve for x<0, we can determine the probability of staying in that range.

4. How does the width of the superposition affect the probability of staying in x<0?

The width of the superposition, determined by the standard deviations of the Gaussians, plays a significant role in the probability of staying in x<0. A wider superposition (larger standard deviations) will result in a lower probability of staying in x<0, as the distribution will be more spread out and have a higher chance of values falling outside of x<0.

5. Are there any real-world applications for understanding the probability of staying in x<0 for a superposition of 2 Gaussians?

Yes, understanding the probability of staying in x<0 for a superposition of 2 Gaussians is important in fields such as finance, engineering, and physics. For example, in finance, this concept can be used to model stock prices and predict the likelihood of a stock falling below a certain threshold. In engineering, it can be used to analyze the reliability of a system or design. In physics, it can be applied to quantum mechanics and the behavior of particles in superposition.

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