What are the dimensions of abs(b_{n})^2 and abs(b(k))^2 in particle functions?

In summary, the conversation discusses the expansion of an arbitrary initial state function for a particle in a box using eigenstates of the Hamiltonian. It also mentions the dimensions of abs(b_{n})^2 and abs(b(k))^2, referring to dimensions in terms of mass, length, and time. The speaker suggests considering the dimensions of ψ(x) in order to understand the concept.
  • #1
iamalexalright
164
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Homework Statement


If an arbitrary intial state function for a particle in a box is expanded in the discrete series of eigenstates of the Hamiltonian relevant to the box configuration, one obtains:

[tex]\psi(x,0) = \Sigma^{\infty}_{n=1}b_{n}(0)\varphi_{n}(x)[/tex]

If the particle is free, we obtain:

[tex]\psi(x,0) = \int^{\infty}_{-\infty}b(k)\varphi(k)dk[/tex]

(a)
What are the dimensions of:
abs(b_{n})^2
and abs(b(k))^2?

I don't understand what they mean by dimensions! any hints?
 
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  • #2
By dimensions they mean, well, dimensions as in dimensional analysis in terms of the standard three, Mass, Length and Time. Start by considering

[tex]\int \psi^{*}(x)\psi(x)\: dx = 1[/tex]

What are the dimensions of ψ(x)? Sort it out from there.
 

FAQ: What are the dimensions of abs(b_{n})^2 and abs(b(k))^2 in particle functions?

What are the dimensions of a projection?

The dimensions of a projection refer to the number of independent variables or features used to represent a dataset. For example, a two-dimensional projection would use two variables to represent the data, while a three-dimensional projection would use three variables.

How do dimensions affect a projection?

The number of dimensions used in a projection can greatly impact the accuracy and interpretability of the resulting visualization. Higher dimensions can lead to a more complex and detailed representation of the data, but may also be more difficult to interpret and visualize.

What is the purpose of using dimensions in a projection?

The use of dimensions in a projection allows for the reduction of high-dimensional data into a lower-dimensional space, making it easier to analyze and comprehend. This can also help to identify patterns and relationships within the data that may not be apparent in the original high-dimensional dataset.

How do you choose the appropriate number of dimensions for a projection?

The appropriate number of dimensions for a projection depends on the specific dataset and the goals of the analysis. It may require trial and error or the use of statistical techniques to determine the optimal number of dimensions. It is important to strike a balance between reducing complexity and maintaining the integrity of the data.

Can dimensions be added or removed from a projection?

Yes, dimensions can be added or removed from a projection depending on the specific needs of the analysis. However, it is important to carefully consider the impact on the accuracy and interpretability of the visualization when making changes to the number of dimensions used.

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