Eigenkets belonging to a range of eigenvalues

In summary, when you want to represent a general ket in a basis consisting of eigenkets each attributed to an eigenvalue in a range, you take the integral of said kets from a to b w.r.t. the eigenvalues. This formalizes the notion of a limit as the sum of a series of individual terms. If the coefficients of the eigenkets change slowly with respect to the eigenvalues, then you can approximate the kets by a continuous basis with a different normalization.
  • #1
Spin One
4
0
When one wants to represent a general ket in a basis consisting of eigenkets each attributed to an eigenvalue in a range, say from a to b, why does one take the integral of said kets from a to b w.r.t. the eigenvalues?
upload_2018-6-25_13-3-42.png

I understand that the integral here plays a role analogous to a sum in the case where a general ket is expressed in terms of eigenkets belonging to discrete eigenvalues, but I don't understand why each vector is multiplied by an infinitesimal change near the eigenvalue it belongs to. Interpreting this integral as the limit of a sum we get:
[P>=Σ[ξ>Δξ (lim.Δξ→0)
where I do not understand the role of Δξ.
 

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  • #3
I looked over the thread about Rigged Hilbert Spaces, and I'm not sure that it completely explained the relationship between continuous and discrete bases.

In a non-rigorous way, you can think of the continuous basis as a limiting case of the discrete basis. However, in going from discrete to continuous, the normalization convention for basis elements changes.

Let me illustrate. Suppose you have an operator ##\Lambda## with discrete eigenvalues ##\lambda_j##. I think in order for the continuum limit to make sense int the most straightforward way, you need to assume that ##\lambda_{j+1} > \lambda_j##, and that there are infinitely many ##\lambda_j##, and that the corresponding eigenstates ##|n\rangle## form a complete orthonormal basis. That means that
  1. If ##n \neq m##, then ##\langle n|m\rangle = 0##
  2. ##\langle n|n\rangle = 1##
  3. If ##|\psi\rangle## is a properly normalized state, then ##|\psi\rangle = \sum_n \langle n|\psi\rangle |n\rangle##
  4. ##\sum_n |\langle n|\psi\rangle|^2 = 1##
Now, if the coefficients ##\langle n|\psi\rangle## change slowly with ##n## (and maybe we also have to assume that ##(\Delta \lambda)_n \equiv \lambda_{n+1} - \lambda_n## remains bounded? I'm not sure...) then we can define a new ket with a different normalization:

##|\lambda_n\rangle \equiv \frac{1}{\sqrt{(\Delta \lambda)_n}} |n\rangle##

In terms of the ##|\lambda_n\rangle##, we have:

##|\psi\rangle = \sum_n (\Delta \lambda)_n \langle \lambda_n |\psi\rangle |\lambda_n\rangle##

The kets ##|\lambda_n\rangle## have a different normalization:

  • ##\langle \lambda_n | \lambda_m \rangle = 0## (if ##m \neq n##)
  • ##\langle \lambda_n | \lambda_n \rangle = \frac{1}{(\Delta \lambda)_n}##
If the states ##|\lambda_n\rangle## change smoothly with ##n##, then this can be approximated by an integral:

##|\psi\rangle = \int d\lambda \langle \lambda |\psi\rangle |\lambda\rangle##
 
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  • #4
So dλ is introduced to make the product between each eigenket and dλ finite, since the eigenkets will be of "infinite length" in the sense of lim.Δλ→0[1/Δλ]. That makes sense. In that case, will the coefficients <λIΨ> be infinitesimal? Otherwise the integral would diverge, even over a finite range.
 
  • #5
Spin One said:
So dλ is introduced to make the product between each eigenket and dλ finite, since the eigenkets will be of "infinite length" in the sense of lim.Δλ→0[1/Δλ]. That makes sense. In that case, will the coefficients <λIΨ> be infinitesimal? Otherwise the integral would diverge, even over a finite range.

Yes - but unless you want to get into non-standard analysis infinitesimals are a load of the proverbial, although used every now and then when speaking informally. I do it but shouldn't really.

There is no way to understand it properly unless you study the references in my link.

Start with Distribution Theory.

Thanks
Bill
 

What are eigenkets and eigenvalues?

Eigenkets and eigenvalues are mathematical concepts used in linear algebra to represent the properties of a linear transformation or operator. Eigenkets are the eigenvectors or basis vectors of a linear transformation, while eigenvalues are the corresponding scalar values that represent how the transformation acts on the eigenkets.

What does it mean for an eigenket to belong to a range of eigenvalues?

An eigenket belonging to a range of eigenvalues means that it is one of the eigenvectors of a particular linear transformation or operator that corresponds to a specific set of eigenvalues. In other words, the eigenket can be multiplied by the linear transformation and result in a scalar multiple of itself, represented by the corresponding eigenvalue.

How are eigenkets and eigenvalues used in quantum mechanics?

In quantum mechanics, eigenkets and eigenvalues are used to represent the energy levels and corresponding wave functions of a quantum system. The eigenvalues represent the possible energy states of the system, while the eigenkets represent the corresponding wave functions.

Can an eigenket belong to multiple eigenvalues?

No, an eigenket can only belong to one eigenvalue. This is because the eigenvalue represents the scalar multiple by which the eigenket is transformed, and an eigenket can only be transformed by one scalar value at a time.

How are eigenkets and eigenvalues calculated?

Eigenkets and eigenvalues can be calculated through a process called diagonalization, where the linear transformation is represented as a matrix and the eigenvalues and corresponding eigenvectors are found through solving a characteristic equation. Alternatively, they can also be calculated using specialized algorithms such as the QR algorithm or the power method.

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