Transition probabilities (basic concepts no math)

In summary, the energy of the unperturbed system does not depend on the time at which the system is in the ground state.
  • #1
RedX
970
3
Does it make sense to speak of the probability of finding a system which was once in the ground state in a higher state after a certain time? Since the Hamiltonian depends on time, once you collapse the wavefunction at that time, the energy you get can't be one of the values of the unperturbed system, but rather the allowed values for the Hamiltonian at the moment you collapse the wavefunction.

For example, take beta decay, where the nucleus gets an extra positive charge and an electron flies out. Because this process happens so quick, the state of a orbital electron in the ground state remains there, but that doesn't mean it has that energy, but instead one has to write the ground state of the unperturbed system as a linear combination of the energy eigenstates for a postive ion nucleus?
 
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  • #2
Well,not really.Once a perturbation is applied,the system's quantum states will undergo a change and it will not make any sense to speak about the probability of finding the system in one of the previous states.The number given by (time-dependent) perturbation theory will not be the amplitude of probability of finding the system in the quantum state [itex] |\psi\rangle [/itex],but the amplitude of probability of TRANSITION from the quantum state [itex]|\psi_{0}\rangle [/itex] to the [itex] |\psi\rangle [/itex]

Daniel.

P.S.We cannot fing the eigenstates of the perturbed Hamiltonian...
 
  • #3
Say the system starts in the ground state at time zero. For adiabatic perturbations, where the Hamiltonian changes slowly over a long period of time, then the system will be found in the ground state of the Hamiltonian at that later time, the ground state of H(t). I guess this has something to do with the time energy uncertainty relation, which I don't understand, as it has nothing to do with the general uncertainty relations for two Hermitian operators. But that's fine I asked a question about the energy-time relationship long ago and didn't understand the answer. We can always find eigenvectors of any operator, whether time is included and not, and the Hilbert is still spanned by these vectors, so any state can be expressed as a linear combo of these vectors. Whether the state collapses into these vectors is physics not maths and I guess depends on the postulates, so that with a time-Hamiltonian you can't find the eigenstates must be something in the postulates.
 

FAQ: Transition probabilities (basic concepts no math)

What are transition probabilities?

Transition probabilities are mathematical concepts used in statistics and probability theory to describe the likelihood of moving from one state or condition to another. They are commonly used in fields such as economics, genetics, and physics to model and predict outcomes of various systems.

How are transition probabilities calculated?

Transition probabilities are typically calculated by dividing the number of transitions from one state to another by the total number of transitions. For example, if there were 100 transitions from state A to state B out of a total of 500 transitions, the transition probability from A to B would be 100/500 or 0.2.

What is the difference between transition probabilities and conditional probabilities?

Transition probabilities describe the likelihood of moving from one state to another, while conditional probabilities describe the likelihood of an event occurring given that another event has already occurred. In other words, transition probabilities focus on changes in states, while conditional probabilities focus on events.

What are some real-world applications of transition probabilities?

Transition probabilities have many practical applications in fields such as finance, marketing, and biology. For example, they can be used to model the movement of stock prices, predict customer behavior in marketing campaigns, and study the spread of diseases in populations.

How can understanding transition probabilities benefit me as a scientist?

As a scientist, understanding transition probabilities can help you make more accurate predictions and decisions based on data. They can also provide insights into the underlying dynamics and patterns of systems you are studying. Additionally, knowledge of transition probabilities can help you effectively communicate your findings to others in your field.

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