Probabilities out of non-normalizable functions?

  • #1
AshIsH_0001
2
0
Homework Statement
There is an electron gun placed at (x,y)=(0,0) in some coordinate system. At x=1nm there is a
screen with two small holes in it, at y =±1 nm. At x=2 nm there is another screen with three holes in it, at y =±1 nm, and at y =0 nm. There is a movable detector placed at x=3 nm. The amplitude for going between points r1 and r2 is given by (notice that this is not normalized):
(See the solution attempt)
(a) Draw a picture of the system. Write down an expression for the probability of finding an
electron in the detector using Dirac notation.
(b) Assume that we have a detector which always lets us know wether or not en electron went
trough the hole at (x, y)=(2, 0). How does the expression for the probability change with this
knowledge?
(c) If the electron is shot out with a momentum of 10 e.V./c, what is the probability of detecting it
at y=0? Use the above equation for the amplitude and use the fact that it only depends on the
distance between two points in order to simplify your expression.
Relevant Equations
<r2|r1> = (e^((i/h)p.r21)/|r21|
Screenshot 2024-02-04 230618.png

a and b were fairly easy to solve; but the c part which actually demands the probability! How are we suppose to fetch the value if the function can't even be normalized; I tried to make some assumptions like making the system bounded; but I don't think that it's the right way to do so... What can be the correct approach? What is the best assumption we can make using the conditions provided??

Would be grateful if anyone could provide me some guidance; any insigth to confirm if I was right from the start for a and b parts would be appreciable...
 
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  • #2
Oberve the small area S on the screen including y=0. Say n particles hit S in total N particle bombardment.
Then we can estimate the value
[tex]\frac{n}{SN}[/tex]
which is probability per unit area.
Could you show us the non-normalizable function you get for investigation ?
 
Last edited:
  • #3
anuttarasammyak said:
Oberve the small area S on the screen including y=0. Say n particles hit S in total N particle bombardment.
Then we can estimate the value
[tex]\frac{n}{SN}[/tex]
which is probability per unit area.
Could you show us the non-normalizable function you get for investigation ?
How can i just calculate the required probability at the detector when placed at (3,0); i tried to solve but ended up some very complicated equations which isn't that easy to solve for probability densities...
 

Related to Probabilities out of non-normalizable functions?

1. What are probabilities out of non-normalizable functions?

Probabilities out of non-normalizable functions refer to the concept of determining the likelihood of certain outcomes when dealing with functions that cannot be normalized. Normalization is a mathematical process that ensures the total probability of all possible outcomes adds up to 1. When dealing with non-normalizable functions, probabilities may not sum up to 1, making it challenging to interpret the results in a traditional probabilistic sense.

2. Can probabilities still be calculated from non-normalizable functions?

Yes, probabilities can still be calculated from non-normalizable functions using techniques such as renormalization or relative probabilities. Renormalization involves adjusting the probabilities to ensure they add up to 1, even if the function itself cannot be normalized. Relative probabilities, on the other hand, compare the likelihood of different outcomes within the non-normalizable function without requiring normalization.

3. How do non-normalizable functions impact probabilistic calculations?

Non-normalizable functions can complicate probabilistic calculations by introducing challenges in interpreting the results. Since the probabilities do not sum up to 1, traditional probabilistic rules may not apply directly. It is important to consider alternative methods for calculating and interpreting probabilities when working with non-normalizable functions.

4. What are some examples of non-normalizable functions in probability theory?

Examples of non-normalizable functions in probability theory include functions with infinite support, such as the Cauchy distribution or the Pareto distribution. These functions have tails that extend infinitely in one or both directions, making it impossible to normalize the function to a total probability of 1. Dealing with such functions requires specialized techniques to calculate probabilities and make meaningful interpretations.

5. How can one address the challenges posed by non-normalizable functions in probabilistic calculations?

Addressing the challenges posed by non-normalizable functions in probabilistic calculations involves using techniques such as truncation, approximation, or reparameterization. Truncation involves limiting the range of the function to make it integrable and allow for normalization. Approximation techniques can be used to estimate probabilities within the non-normalizable function. Reparameterization involves transforming the function into a more manageable form that allows for probabilistic calculations.

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