Probability Distribution for Time of Measurement?

In summary: Thank you for sharing! In summary, there is a concept of "time of arrival" in quantum mechanics, which deals with the probability of a detector registering a measurement within a certain time interval. There is ongoing research on this topic, and one can find more information by searching for "time of arrival" on the web and/or arXiv. One relevant paper is "Time-of-arrival in quantum mechanics" by Grot, Rovelli, and Tate.
  • #1
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Say we have an electron in a position eigenstate [itex] \delta(P-x) [/itex] at point [itex] P [/itex] at time [itex] t_0 [/itex]. We also have a detector at another point Q. At [itex] t_0 [/itex], the probability of the detector registering the electron is zero. After a certain time [itex] T [/itex], the time evolution of the wave function generates a non-zero probability for the electron to be found at Q, but what can we say about the time of measurement? Is there a function [itex] f(t) [/itex] such that [itex] f(t)dt [/itex] gives the probability that the detector will register a measurement in the interval [itex] (t, t+dt) [/itex]?
 
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  • #2
Excellent question!

Sorry for not answering it, though. I merely wanted to say that if you had not asked this, I might have been asking the same thing at some point. I had actually already started to preparing some kind of question about this topic (that's why I think this is an excellent question :biggrin:), but I've still been too busy with some other problems, and replies to my other questions.

When you know that some theory exists, it is a simple thing to tell about it.

But when you don't know that some theory exists, it becomes dangerous to claim that it wouldn't exist. It could be that you just haven't heard about it, but somebody else has, and somebody could prove you wrong.

This probability density with respect to time seems to belong into this difficult category of topics, where everybody merely wants to keep mouth shut in order to avoid saying anything wrong. I've been speculating that some kind of "effective collapsing density" should exist, but I don't really know how it should work.

Fortunately we have some scientists who are ready to go through the effort of actually finding out what is known and what is not known, and I'm probably not wrong to guess that Demystifier belongs into this group? :smile: I'm looking forward to hear his comment on this (sorry for creating pressure :wink:)
 
  • #3
You can get more info by searching the web and/or arXiv for "time of arrival"
 
  • #5
Very interesting, meopemuk.
 

1. What is a probability distribution for time of measurement?

A probability distribution for time of measurement is a mathematical function that describes the likelihood of a particular outcome occurring at a specific time. It shows the range of possible outcomes and their corresponding probabilities.

2. How is a probability distribution for time of measurement calculated?

A probability distribution for time of measurement is calculated by dividing the number of desired outcomes by the total number of possible outcomes. This is known as the probability of an event and can range from 0 (impossible) to 1 (certain).

3. What are the different types of probability distributions for time of measurement?

There are several types of probability distributions for time of measurement, including the normal distribution, binomial distribution, and Poisson distribution. Each of these distributions has its own unique characteristics and uses.

4. Why is understanding probability distribution for time of measurement important in science?

Understanding probability distribution for time of measurement is important in science because it allows scientists to make predictions and draw conclusions based on data. It also helps to determine the reliability and accuracy of experimental results.

5. How can probability distribution for time of measurement be applied in real-world scenarios?

Probability distribution for time of measurement can be applied in a variety of real-world scenarios, such as predicting the likelihood of a disease outbreak, determining the probability of a stock market crash, or estimating the chances of a natural disaster occurring. It can also be used in quality control processes to identify potential defects in products.

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