Normalization to delta distribution

In summary: An operator with a continuous spectrum and eigenvalues s, and corresponding eigenfunctions that obey the continuity equation \delta(\epsilon-\epsilon') = 0, is called a Hamiltonian. For example, the free Hamiltonian for a particle in a two-dimensional space has energy eigenvalues -1, 0, and 1, and corresponding eigenfunctions are v_{p}(q), where p is the position of the particle. The energy is degenerate, and the most convenient way to work with the energy is to use the momentum eigenfunctions.
  • #1
Jano L.
Gold Member
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Do you know some example of an operator, other than momentum or position, that has (at least partially) continuous spectrum with eigenvalues [itex]s[/itex], and the corresponding eigenfunctions obey

[tex]
(\Phi_s,\Phi_s') = \int \Phi_s^*(q) \, \Phi_{s'} (q)~ dq = \delta(s-s')~?
[/tex]

EDIT
For example, Hamiltonian of a free particle does not work with this, because the integral

[tex]
\int \Phi_\epsilon^*(q) \, \Phi_{\epsilon'} (q)~ dq
[/tex]

with [itex]\Phi_\epsilon(q) = e^{i \sqrt{2m\epsilon} \,q/\hbar}[/itex] does not equal to [itex]\delta(\epsilon - \epsilon')[/itex].
 
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  • #2
Why not. Let's set
[tex]\Phi_{\epsilon}(q)=N(\epsilon) \exp(\mathrm{i} \sqrt{2m \epsilon} q).[/tex]
This is indeed the generalized eigenfunction of the free Hamiltonian
[tex]\hat{H}=-\frac{1}{2m} \frac{\mathrm{d}^2}{\mathrm{d} q^2}[/tex]
in position-space representation.

Now we have
[tex]\langle \epsilon'|\epsilon \rangle = \int_{\mathbb{R}} \mathrm{d} q \Phi_{\epsilon'}^*(q) \Phi_{\epsilon}(q) = 2 \pi |N(\epsilon)|^2 \delta \left [\sqrt{2m}(\sqrt{\epsilon}-\sqrt{\epsilon'} \right]=\sqrt{8m \epsilon} \pi |N(\epsilon)|^2 \delta(\epsilon-\epsilon').[/tex]
Thus setting
[tex]N(\epsilon)=\frac{1}{\sqrt{\sqrt{8m \epsilon} \pi}}[/tex]
normalizes your functions to a [itex]\delta[/itex] distribution.

Of course, this is not a complete set, because also
[tex]\Psi_{\epsilon}(q)=N(\epsilon) \exp(-\mathrm{i} \sqrt{2m \epsilon} q)[/tex]
are eigenfunctions of the Hamiltonian with the same eigenvalue [itex]\epsilon[/itex].

Each energy eigenvalue is thus twofold degenerate. You can classify the energy eigenstates further by parity. That also explains the degeneracy: the parity operation commutes with the Hamiltonian and thus can be simultaneously diagonalized. The energy-parity eigenstates are given by
[tex]u_{\epsilon,P}(q)=\frac{1}{\sqrt{2}} [\Phi_{\epsilon}(q) + P \Psi_{\epsilon}(q)] \quad \text{with} \quad P \in \{-1,+1\}.[/tex]

The energy is degenerate, and the generalized basis built by[itex]\Phi_{\epsilon}[/itex] and [itex]\Psi_{\epsilon}[/itex] or [itex]u_{\epsilon,P}[/itex] is pretty inconvenient to work with. Much more convenient is the basis of momentum eigenfunctions,
[tex]v_{p}(q)=\frac{1}{\sqrt{2 \pi}} \exp(-\mathrm{i} p q), \quad p \in \mathbb{R}[/tex]
and has all nice properties of the above constructed energy-parity eigenbasis, because of course the [itex]v_{p}[/itex] are also generalized energy eigenstates.
 
  • #3
Thank you very much! It is much more clear now. I didn't realize one can normalize [itex]\Phi_\epsilon[/itex]'s by dividing by [itex]\sqrt{\epsilon}[/itex].
 
  • #4
Oh this somehow got to the classical physics forum, I apologize for that.
 
  • #5


One example of an operator with continuous spectrum and eigenfunctions that satisfy the above equation is the energy operator in quantum mechanics. The eigenfunctions of the energy operator are the states of the system with definite energy, and the spectrum of the operator is continuous since the energy can take on any real value. The eigenfunctions are given by the wavefunction \Psi_E(x) = e^{iEt/\hbar}, where E is the energy and t is time. The inner product of two eigenfunctions with different energies is given by the integral \int \Psi_E^*(x)\Psi_{E'}(x)~dx = \delta(E-E'), which satisfies the condition for normalization to the delta distribution. This is a fundamental result in quantum mechanics and is used in many calculations and applications.
 

1. What is normalization to delta distribution?

Normalization to delta distribution is a mathematical process used to convert a continuous probability distribution into a discrete probability distribution, where the sum of all probabilities is equal to 1. It involves dividing each probability value by the total sum of probabilities to ensure that the distribution is properly normalized.

2. Why is normalization to delta distribution important?

Normalization to delta distribution is important because it allows for the comparison and analysis of data sets that have different scales and units. It also ensures that the probabilities in a distribution represent the true likelihood of each event occurring.

3. How is normalization to delta distribution performed?

The normalization to delta distribution is performed by dividing each probability value by the sum of all probabilities. This results in a new set of probability values that sum to 1, creating a proper discrete probability distribution.

4. What is the role of the delta function in normalization to delta distribution?

The delta function, also known as the Dirac delta function, is used to represent an infinitely narrow and tall spike at the origin. In normalization to delta distribution, this function is used to "spread out" the probabilities of the continuous distribution into discrete probabilities that can be summed to 1.

5. Are there any limitations to normalization to delta distribution?

Yes, there are some limitations to normalization to delta distribution. It assumes that the data follows a continuous probability distribution, which may not always be the case. It also cannot be used if the probability values are negative or if the total sum of probabilities is not equal to 1.

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