Probability is inverse of velocity?

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Discussion Overview

The discussion revolves around the relationship between probability and velocity in the context of classical mechanics, particularly focusing on trapped particles in a harmonic potential. Participants explore the implications of velocity being zero at certain points and how this affects the probability density function. Additionally, there is a side discussion on finding the radial velocity as a function of position or time for a central force problem with a specific potential.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning
  • Homework-related

Main Points Raised

  • Some participants propose that the probability of finding a trapped particle at a location x is intuitively proportional to the inverse of its velocity, leading to a probability density that is undefined at points where velocity is zero.
  • Others argue that the relationship between probability and velocity may not be straightforward, questioning why probability should depend solely on the first-order rate of change (velocity) rather than considering higher-order effects like acceleration.
  • A participant mentions that in continuous probability distributions, the probability of finding a particle at an exact point is zero, and only intervals yield non-zero probabilities.
  • There is a discussion about integrating the probability density to find the probability of locating a particle within a specific range, with some expressing curiosity about numerical methods for handling cases where the velocity becomes imaginary.
  • Participants share equations related to central force problems and discuss the challenges of finding closed-form solutions for specific potentials, such as those proportional to r^3.
  • One participant suggests numerical methods for finding classical turning points and shares strategies for solving equations numerically, referencing a specific book for further guidance.

Areas of Agreement / Disagreement

Participants express differing views on the relationship between probability and velocity, with no consensus reached on the validity of the proposed inverse relationship. The discussion on numerical methods and solving for turning points also reflects a range of experiences and approaches, indicating that multiple competing views remain.

Contextual Notes

Some participants note the limitations of their approaches, such as the dependence on specific potential forms and the challenges of integrating probability densities that approach infinity at certain points. The discussion also highlights the complexity of finding solutions for different types of potentials.

omni-impotent
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Dear all,

Intuitively, the probability of finding a trapped particle at a given location x should be proportional to the inverse of the velocity of the particle. But for even the simplest of systems, such as 1D harmonic potential SHM, the velocity is zero at the two end points (+A & -A) of the motion. This makes 1/v(x) not defined for x=+A & -A. How is this intuitively possible according to classical mechanics only?

On a side note, I am actually trying to find rdot as a function of r or t for a central force problem with potential proportional to r^3. Anyone tackled such a problem? :)

Cheers.
 
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omni-impotent said:
Intuitively, the probability of finding a trapped particle at a given location x should be proportional to the inverse of the velocity of the particle.

More precisely, the probability density at x is proportional to 1/v(x).

But for even the simplest of systems, such as 1D harmonic potential SHM, the velocity is zero at the two end points (+A & -A) of the motion. This makes 1/v(x) not defined for x=+A & -A. How is this intuitively possible according to classical mechanics only?

In order to get an actual probability of finding the particle in the range a < x < b, you must integrate the probability density between x = a and x = b. I expect you will find that even though the probability density approaches infinity as x approaches +A or -A, an integral which includes one or both of those points as a limit nevertheless converges.
 
omni-impotent said:
Dear all,

Intuitively, the probability of finding a trapped particle at a given location x should be proportional to the inverse of the velocity of the particle.

What tells you this? I'm not sure if there is some principle or anything as such.. but if you look at it, why do you consider that the probability of a desired quantity needs to be inversely proportional to only it's first order rate of change. In the case you presented, the places where velocity was zero, the second order rate of change i.e. acceleration is maximum.

Other than that, the case is of a continuous probability distribution. In that case, the probability that the object will be at any point is always zero. It is only between an interval that you can find the probability of occurrence.

P.S: I like ur nickname :D
 
In order to get an actual probability of finding the particle in the range a < x < b, you must integrate the probability density between x = a and x = b. I expect you will find that even though the probability density approaches infinity as x approaches +A or -A, an integral which includes one or both of those points as a limit nevertheless converges.

That makes sense. :) It might end up that I do not have an analytic expression for the velocity. I wonder if a numerical integration of the inverse velocity would yield converging results... guess I will need to play around in Matlab.

Thanks for your help.
 
rohanprabhu said:
What tells you this? I'm not sure if there is some principle or anything as such.. but if you look at it, why do you consider that the probability of a desired quantity needs to be inversely proportional to only it's first order rate of change. In the case you presented, the places where velocity was zero, the second order rate of change i.e. acceleration is maximum.

The probability density most definitely depends only on the velocity. There are no higher order terms. Think of the radial distance probability density function of a circular motion problem. It is of course a delta-function at a radius R, this is because the magnitude of the radial velocity is zero while the radial acceleration is finite.

P.S: I like ur nickname :D

Thanks. It came to me in my sleep. :)
 
omni-impotent said:
On a side note, I am actually trying to find rdot as a function of r or t for a central force problem with potential proportional to r^3. Anyone tackled such a problem?

<br /> dr/dt = \sqrt{2/m}\left(E - V(r) - \frac{L^2}{2mr^2}\right)^{1/2}<br />

Put in whatever potential you wish for V(r). L is the angular momentum.
E is the total energy.
 
Last edited:
pkleinod said:
<br /> dr/dt = \sqrt{2/m}\left(E - V(r) - \frac{L^2}{2mr^2}\right)^{1/2}<br />

Put in whatever potential you wish for V(r). L is the angular momentum.
E is the total energy.

Yes I have gotten that far. But as you can see from the equation, there are regions where dr/dt is imaginary. Physically, the particle is forbidden to enter these regions. That is, there exists a r_{min} and a r_{max} given by solving:

<br /> E - V(r) - \frac{L^2}{2mr^2} = 0<br />

Trying to find these has been the problem. I used the symbolic toolbox in MATLAB and I can get solutions where the potential V(r) is proportional to r^-1, r, r^2, r^4. But when I put in the case I am interested in, r^3, it says that a closed form solution can't be found. Do you have idea of how one goes about solving this? Maybe I should make a new post on it.
 
omni-impotent said:
Do you have idea of how one goes about solving this?

Yes indeed. I have done this thousands of times for many different potentials V(r) in connection with a program that computes classical trajectories to simulate chemical reactions. For most potentials there is no closed-form solution, so you have to solve for the classical turning points numerically. For each turning point you need to do the following:

1) You need a rough estimate of the root of the equation.
2) Then you need to bracket the root.
3) Once the root is localised, you must use an algorithm that homes in on the solution. I used Newton's method in those cases where the derivative of V(r) wrt r was known; otherwise, I used the bisection method (binary chop), which is slower but safer. There are also canned programs using combined strategies.

Look at the book "Numerical Recipes" by Press, Flannery, Teukolsky, and Vetterling.
 

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