Translation Invariant: Seeing it Intuitively & Mathematically

In summary, the conversation discusses the concept of translation invariance in relation to the Hamiltonian and its diagonalization in the momentum basis. The hydrogen atom Hamiltonian is an example of a translationally invariant Hamiltonian, but is not diagonal in the momentum basis. The conversation also suggests a way to rewrite the Hamiltonian in terms of new variables to show that the total momentum, Hamiltonian, and orbital angular momentum form a complete set of observables.
  • #1
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How do I see that when my hamiltonian is translation invariant i.e. H = H(r-r') it means that it is diagonal in the momentum basis? I can see it intuitively but not mathematically.
 
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  • #2
If you can show that ##[\hat p, \hat H] = 0## then you have shown that ##\hat p## and ##\hat H## can be simultaneously diagonalized, which is what you are after.
 
  • #3
With your definition of translational invariance (if I understood it, it is invariance with respect to coordinate shifts) the hydrogen atom Hamiltonian is translationally invariant, because the potential energy term is function of ##|\mathbf r_1 - \mathbf r_2|##. But this Hamiltonian is not diagonal in the momentum basis.
 
  • #4
the 'true' hydrogen atom Hamiltonian should be diagonal in the momentum basis. But usually people just talk about a central potential, and ignore the nucleus, since it is much heavier than the electron. Once we make this approximation of a central potential and ignore the nucleus, the potential energy term now depends on absolute spatial position, so the Hamiltonian is translationally variant.
 
  • #5
Even if the Hamiltonian
$$
\hat{H} = \frac{\hat{\mathbf{p}}_1^2}{2m_1} + \frac{\hat{\mathbf{p}}_2^2}{2m_2} - \frac{Kq^2}{4\pi} \frac{1}{|\mathbf r_1 - \mathbf r_2|},
$$
is translationally invariant, I do not think it is diagonal in the momentum basis ##\mathbf p_1, \mathbf p_2##. Only the sum of the two momentum operators commutes with the Hamiltonian, but the momentum operators individally do not.
 
  • #6
Rewrite the Hamiltonian in terms of the CM motion and the relative motion, i.e., in terms of the new variables
[tex]\vec{R}=\frac{m_1 \vec{r}_1 + m_2 \vec{r}_2}{m_1+m_2}, \quad \vec{P}=\vec{p_1}+\vec{p}_2, \quad \vec{r}=\vec{r}_1-\vec{r}_2, \quad \vec{p}=\frac{m_2 \vec{p}_1-m_1 \vec{p}_2}{m_1+m_2}.[/tex]
Then you can show that [itex]\vec{P}[/itex], [itex]\vec{H}[/itex], [itex]\vec{l}^2[/itex] and [itex]l_z[/itex] with the orbital angular momentum of the relative motion
[tex]\vec{l}=\vec{r}\times \vec{p}[/tex]
form a complete set of observables.
 

Related to Translation Invariant: Seeing it Intuitively & Mathematically

What is translation invariant?

Translation invariant refers to a property of an object or system that remains unchanged under translation, which is the process of moving or shifting the object or system without changing its shape or size.

How does translation invariance relate to math?

In math, translation invariance is closely related to symmetry and is often used to simplify mathematical problems. It allows us to focus on the underlying structure or pattern of a system without being affected by its position or location.

Can you give an example of translation invariance in the real world?

One common example of translation invariance in the real world is a chessboard. The rules and strategies of the game remain the same regardless of where the pieces are positioned on the board. This is because the game is translation invariant, meaning the outcome is not affected by the position of the pieces but rather the moves they make.

What is the difference between translation invariance and rotational invariance?

While translation invariance refers to the preservation of an object or system under translation, rotational invariance refers to the preservation under rotation. In other words, a system is rotationally invariant if its properties remain unchanged when rotated around a fixed point.

Why is translation invariance important in science and engineering?

Translation invariance plays a crucial role in science and engineering as it allows us to simplify and generalize problems, making them easier to solve. It also helps us to better understand the underlying patterns and structures of complex systems, leading to advancements in various fields such as physics, computer science, and image processing.

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