One dimensional problems. More particles.

In summary: The probability of getting 60 heads and 40 tails in 100 coin tosses is actually quite high, around 0.0108. This is because the binomial distribution describes the probability of getting a certain number of successes (heads) in a fixed number of trials (coin tosses) with a known probability of success (getting heads). Therefore, it is possible for 60 particles to be in one region and 40 particles to be in the other, just as it is possible to get 60 heads and 40 tails in 100 coin tosses.
  • #1
LagrangeEuler
717
20
If I have one dimensional problem with many particles that are all in same ##|\psi\rangle## state is it equal to one dimensional problem of one particle in state ##|\psi\rangle##.
If I have for example 50 particles in some state ##\psi(x)## in infinite potential well and that state is symmetric around ##\frac{a}{2}## such that ##\int^{\frac{a}{2}}_0|\psi(x)|^2dx=\frac{1}{2}##. Is in that case true statement that ##25## particles are in the region ##0<x<\frac{a}{2}## and 25 are in the region ##\frac{a}{2}<x<a##?
 
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  • #2
I would say not. The average may be 25 on each side though.
 
  • #3
LagrangeEuler said:
If I have one dimensional problem with many particles that are all in same ##|\psi\rangle## state is it equal to one dimensional problem of one particle in state ##|\psi\rangle##.
If I have for example 50 particles in some state ##\psi(x)## in infinite potential well and that state is symmetric around ##\frac{a}{2}## such that ##\int^{\frac{a}{2}}_0|\psi(x)|^2dx=\frac{1}{2}##. Is in that case true statement that ##25## particles are in the region ##0<x<\frac{a}{2}## and 25 are in the region ##\frac{a}{2}<x<a##?
Think about the classical case. 50 particles, each independently with probability 1/2 to be on the left, probability 1/2 to be on the right. How many do you expect to find on each side? Hint: can you say "binomial distribution"? :wink:

The quantum case is no different.
 
  • #4
My question is if I have coin, probability to get head is ##\frac{1}{2}##. If I throw the coin 100 times I could get for example 60 times head. Then in this case is it possible that ##60## particles be in the region ##0<x<\frac{a}{2}## and 40 in the region ##\frac{a}{2}<x<a##?
 
  • #5
You need to count all the permutations which are the same, hence use the binomial distribution.
 
  • #6
LagrangeEuler said:
My question is if I have coin, probability to get head is ##\frac{1}{2}##. If I throw the coin 100 times I could get for example 60 times head. Then in this case is it possible that ##60## particles be in the region ##0<x<\frac{a}{2}## and 40 in the region ##\frac{a}{2}<x<a##?

Yes, this is certainly possible.
 

Related to One dimensional problems. More particles.

1. What is a one-dimensional problem?

A one-dimensional problem is a scientific or mathematical problem that involves only one independent variable and one dependent variable. This means that the problem can be described using only one axis or dimension.

2. What are some examples of one-dimensional problems?

Examples of one-dimensional problems include motion along a straight line, simple harmonic motion, and heat conduction in a rod.

3. How are one-dimensional problems different from multi-dimensional problems?

One-dimensional problems are different from multi-dimensional problems because they only involve one independent variable and one dependent variable, while multi-dimensional problems involve multiple independent and dependent variables. This makes one-dimensional problems simpler to solve and visualize.

4. How do you solve a one-dimensional problem?

The specific approach for solving a one-dimensional problem will depend on the specific problem and the scientific or mathematical techniques involved. However, in general, the problem can be solved by setting up equations and using algebra, calculus, or other mathematical methods to solve for the unknown variable.

5. What are some real-world applications of one-dimensional problems?

One-dimensional problems have numerous real-world applications, including predicting the motion of objects such as projectiles or planets, analyzing the behavior of simple mechanical systems, and understanding the flow of heat or electricity in one-dimensional systems. They are also commonly used in engineering and physics to model and solve problems in various fields.

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