Factorising expectation values

In summary, the conversation discusses the factorization of expectation values in 3-D momentum space for a harmonic oscillator. The factorization only holds for uncorrelated momentum components and is easily seen in the probability distribution. The conversation also includes a detailed example of a 3-D harmonic oscillator and the use of the Fock basis to show the factorization. Finally, the conversation mentions the calculation of a ground-state average in Dirac notation and the use of the factorization result for each expectation value.
  • #1
dyn
773
61
Hi.
I came across the following in the solution to a question I was looking , regarding expectation values of momentum in 3-D
< p12p22p32 > = < p12 > < p22 > <p32 >
ie. the expectation value has been factorised. I can't figure out why this is true and also why it doesn't apply to the following. eg. < p12 > ≠ < p1 > < p1 > ?
Any thoughts would be appreciated. Thanks
 
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  • #2
It's only true if the different momentum components are uncorrelated, i.e., if the probability distribution factorizes;
$$P(\vec{p})=P_1(p_1) P_2(p_2) P_3(p_3).$$
Then it's very easy to see that the first equation is correct.

Of course, this does not hold for ##\langle p_1^2 \rangle##, because obviously
$$\langle p_1^2 \rangle=\int_{\mathbb{R}} \mathrm{d} p_1 p_1^2 P(p_1),$$
while
$$\langle p_1 \rangle^2=\left [\int_{\mathbb{R}} \mathrm{d} p_1 p_1 P(p_1) \right]^2 \neq \langle p_1^2 \rangle.$$
 
  • #3
Thanks for your reply. The question actually concerned expectation values of the harmonic oscillator using ladder operators and bras and kets
vanhees71 said:
It's only true if the different momentum components are uncorrelated, i.e., if the probability distribution factorizes;
$$P(\vec{p})=P_1(p_1) P_2(p_2) P_3(p_3).$$
Then it's very easy to see that the first equation is correct.
How would I know that the probability distribution factorises and how does this lead to the first equation being correct ?
 
  • #4
It's always good to explain the example you have in mind in detail, so that we can answer questions precisely.

If you have a 3D harmonic oscillator with the Hamiltonian
$$\hat{H}=\frac{\hat{\vec{p}}^2}{2m} + \frac{m}{2} \sum_{j=1}^3 \omega_j^2 \hat{x}_j^2,$$
obviously the number operators
$$\hat{N}_j=\hat{a}_{j}^{\dagger} \hat{a}_j$$
commute, and you can build an orthonormal basis of common eigenvectors (the Fock basis of three independent harmonic oscillators). Since each of the operators ##\hat{N}_j## only depend on ##\hat{x}_j## and ##\hat{p}_j## these states are obviously factorizing in the above described way, i.e., in the momentum representation you have
$$u_{N_1,N_2,N_3}(\vec{p})=u_{N_1}(p_1) u_{N_2}(p_2) u_{N_3}(p_3).$$
It's also clear that these are are complete set of energy eigenstates with eigenvalues
$$E_{N_1,N_2,N_3}=\sum_{j=1}^3 \frac{\hbar \omega_j}{2} (2N_j+1).$$
 
  • #5
Thanks. The actual question is " calculate the following ground-state average for a 3-D harmonic oscillator < 0 | p12p22p32 | 0 > .
The answer then states the word " factorisation" and states < p12p22p32 > = <p12> <p22 > <p32> and then each expectation value is calculated in the usual way. I would just like to know how to show this result using Dirac notation
 

Related to Factorising expectation values

1. What is the purpose of factorising expectation values?

Factorising expectation values is used to simplify complicated expressions and equations involving multiple variables. It allows us to break down a complex problem into smaller, more manageable parts for analysis.

2. How do you factorise expectation values?

To factorise expectation values, we follow a set of rules and techniques such as grouping, common factorisation, and difference of squares. We also make use of algebraic identities and properties to simplify the expression.

3. Can factorising expectation values be applied to all types of equations?

Yes, factorising expectation values can be applied to a wide range of equations, including polynomial, rational, and exponential equations. It is a fundamental technique in algebra and is used extensively in mathematics, physics, and other scientific fields.

4. How does factorising expectation values help in solving equations?

Factorising expectation values allows us to reduce the complexity of an equation and make it easier to solve. By breaking down an equation into smaller parts, we can identify common factors and simplify the expression, making it easier to solve for the unknown variable.

5. Are there any limitations to factorising expectation values?

While factorising expectation values is a powerful technique, it does have its limitations. It may not always be possible to factorise an equation, and even when it is possible, it may not always lead to a solution. In some cases, the resulting expression after factorisation may be more complicated than the original equation.

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