Normalization Factor Homework: Calculating N and Measuring S Eigenvalues

I hope you will find it interesting and will be able to understand it better in the future. Good luck!In summary, the conversation discussed a quantum system with a measurable property represented by the observable S with possible eigenvalues nh, where n = -2, -1, 0, 1, 2. The corresponding eigenstates have normalized wavefunctions \psi_{n}. The system is prepared in the normalized superposition state given by \psi = \frac{1}{N}(\psi_{-2}+2\psi_{-1}+4\psi_{1}-6\psi_{2}), where N is a normalization factor. The conversation then discussed the calculation of N in order to determine the probability for measurements of
  • #1
benedwards2020
41
0

Homework Statement



A quantum system has a measurable property represented by the observable S with possible eigenvalues nh, where n = -2, -1, 0, 1, 2. The corresponding eigenstates have normalized wavefunctions [tex]\psi_{n}[/tex]. The system is prepared in the normalized superposition state given by

[tex]\psi = \frac{1}{N}(\psi_{-2}+2\psi_{-1}+4\psi_{1}-6\psi_{2}[/tex]

Where N is a normalization factor

(i) Calculate N

(ii) Write down the probability for each of the following measurements os S: -h, 0, 2h


The Attempt at a Solution



Given that the wavefunction is normalised, the sum of the squared moduli of the coefficients equals 1, so

[tex]\left(\frac{1}{N}\right)^2 = \left(\frac{1}{N}\right)^2\left(+\frac{2}{N}\right)^2+\left(\frac{4}{N}\right)^2+\left(\frac{-6}{N}\right)^2[/tex]

Which equals

[tex]\frac{1}{N}^2=\frac{1}{N}+\frac{4}{N}+\frac{16}{N}+\frac{36}{N}[/tex]

I'm getting a bit lost from here though
 
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  • #2
The condition you are looking for is [tex] \left< \psi | \psi \right> = 1 [/tex] (or any other value you'd normalize to), I don't understand what you're saying about the "squared moduli of the coefficients" (possible due to my lack in english), but your equations defenitely does not reflect the condition I stated (an addend independent of N is missing).
 
  • #3
Timo said:
I don't understand what you're saying about the "squared moduli of the coefficients"

The modulus of a complex number is its "length". What Ben is saying is that if a wavefunction iis written as a linear combination of orthonormal wavefunctions, then the squared "length" of the wavefunction is the sum of the squares of the "lengths" of the coefficients for the linear combination.

Ben:

1) you should understand why this is true;

2) from where did the left side of your second-last equation come;

3) are you sure about the denominators on the right side of your last equation?
 
  • #4
Hmm... I'm probably missing some vital piece of knowledge here... My books aren't very explicit in describing this situation... In fact I am finding the whole quantum physics stuff a bit hard to follow... But anyhow

For the points you raise...

(i) I understand your point about the squared length of the wavefunction being equal to the sum of the squares

(ii) I see what you mean about the left hand side of the 2nd equation... I should have it down as

[tex]\frac{1}{N}\left(\frac{1}{N}+\frac{4}{N}+\frac{16}{N}+\frac{36}{N}\right) = 1[/tex]


(ii) I'm not sure I understand about the denominators on the right hand side. If we have

[tex]\frac{1}{N}\left(1+4+16+36) = 1[/tex]


Isn't this the same as saying

[tex]\frac{1+4+16+36}{N} = 1[/tex]?

If I solve for N here I get 57
 
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  • #5
benedwards2020 said:
I'm not sure I understand about the denominators

In your last post, how did you go from the first equation to the second equation?
 
  • #6
I just multiplied out the brackets as you would normally... Something tells me I'm wrong here...
 
  • #7
benedwards2020 said:
I just multiplied out the brackets as you would normally... Something tells me I'm wrong here...

What does

[tex]\frac{2}{3} \times \frac{5}{7}[/tex]

equal?
 
  • #8
[tex]\frac{2 \times 5}{3 \times 7}=\frac{10}{21}[/tex]


Am I right in saying that

[tex]\frac{1}{N}\left(\frac{1}{N}+\frac{4}{N}+\frac{16} {N}+\frac{36}{N}\right) = 1[/tex]

but wrong in how I've multiplied it out?
 
  • #9
What does

[tex]\frac{1}{N} \times \frac{36}{N}[/tex]

equal?
 
  • #10
Ah.. I see what you mean..

[tex]\frac{1}{N} \times \frac{36}{N} = \frac{1}{N^2}[/tex]

Therefore I should have

[tex]\frac{1}{N^2}+\frac{4}{N^2}+\frac{16}{N^2}+\frac{36}{N^2} = 1[/tex]
 
  • #11
benedwards2020 said:
Ah.. I see what you mean..

[tex]\frac{1}{N} \times \frac{36}{N} = \frac{1}{N^2}[/tex]
No

benedwards2020 said:
Therefore I should have

[tex]\frac{1}{N^2}+\frac{4}{N^2}+\frac{16}{N^2}+\frac{36}{N^2} = 1[/tex]
Yes.
So
[tex]\frac{57}{N^2} = 1[/tex].
What is [itex]N[/itex] then?
 
  • #12
Sorry... Of course

[tex]\frac{1}{N} \times \frac{36}{N} = \frac{36}{N^2}[/tex]



So [tex]\frac{57}{N^2}[/tex]

has N = 7.5498
 
  • #13
You mean [itex]N \approx 7.5498[/itex], or [itex]N = \sqrt{57}[/itex].
Well done.
 
  • #14
So the probability for each of the measurements S: -h, 0, 2h
will be simply

P(-h) = -1/(sqrt(57))
P(0) = 0
P(2h) = 4/(sqrt(57))

Is this right?
 
  • #15
benedwards2020 said:
So the probability for each of the measurements S: -h, 0, 2h
will be simply

P(-h) = -1/(sqrt(57))
P(0) = 0
P(2h) = 4/(sqrt(57))

Is this right?

Ouch. Those are amplitudes, not probabilities. Probability can't be negative. What's the relation between amplitude and probability? Wait a minute, they aren't even that. P(-h) should be associated with psi(-1) which has a coefficient of 2, not -1. Similarly for P(2h).
 
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  • #16
Oh dear... Back to the books again I think... My paper asks for probabilities for each of the measurements and gives an example similar to the answers I just gave... I can honestly say that quantum stuff really isn't my forte!

What should I be looking out for when calculating probabilities?
 
  • #17
P(-h) corresponds to psi(-1), which has a coefficient of 2/sqrt(57), right? As it says in the problem statement, probability is the squared modulus of the coefficient. I.e. 4/57. Check that the sum of ALL the probabilities adds to one. It will help you understand why you normalized the function.
 
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  • #18
Ah... So for P(2h) this corresponds to psi(+2) which has coefficient of -6/sqrt(57) yes? which gives us by modulus square of coefficients 36/57?
 
  • #19
benedwards2020 said:
Ah... So for P(2h) this corresponds to psi(+2) which has coefficient of -6/sqrt(57) yes? which gives us by modulus square of coefficients 36/57?

Now you're cooking. What's the sum of probabilities of all four possible states?
 
  • #20
Ok, that would be

[tex]\frac{1}{57}+\frac{4}{57}+\frac{16}{57}+\frac{36}{57}[/tex]

which = 1
 
  • #21
benedwards2020 said:
Ok, that would be

[tex]\frac{1}{57}+\frac{4}{57}+\frac{16}{57}+\frac{36}{57}[/tex]

which = 1


Great =)

Quantum mechanics is a bit hard in the begining, don't give up!
 

FAQ: Normalization Factor Homework: Calculating N and Measuring S Eigenvalues

What is normalization factor homework?

Normalization factor homework refers to a set of mathematical exercises in which students are required to calculate a normalization factor (N) and measure S eigenvalues. This is a common topic in statistics and data analysis courses.

Why is normalization important in data analysis?

Normalization is important because it allows for fair comparisons between different sets of data. It removes the effects of differences in scale and magnitude, making it easier to identify patterns and trends in the data.

How do you calculate the normalization factor (N)?

The normalization factor (N) is calculated by dividing the original value of a data point by the sum of all the data points in the set. This ensures that the sum of all the normalized data points equals 1.

What are S eigenvalues and how are they measured?

S eigenvalues are a measure of the variability or spread of a set of data. They are calculated by taking the square root of the variance, which is the average of the squared differences between each data point and the mean of the set.

How is normalization factor homework applicable in real life?

Normalization factor homework is applicable in various fields such as finance, economics, and social sciences. It allows for accurate comparisons and analysis of data, which is essential in making informed decisions and drawing meaningful conclusions.

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