There is no scientific content in this webpage title.

In summary, the conversation discusses an integral involving two normalized 1D harmonic oscillator wave functions and the possibility of approximating it under certain conditions. However, it is pointed out that the dimensions do not match and there is no physical relevance to the integrals in question. The conversation ends with a mention of the Division Lemma by Euclid.
  • #1
jfy4
649
3
Hi,

I am trying to figure out the following integral. I have two normalized 1D harmonic osccilator wave functions [itex]\psi_{n}(x)[/itex] and [itex]\psi_{m}(x)[/itex] and I would like to integrate
[tex]
\int_{\text{all space}} |\psi_{n}(x)|^2 |\psi_{m}(x)|^2 dx
[/tex]
for [itex]m\neq n [/itex]. I would also be interested in knowing for what conditions on [itex]m[/itex] and [itex]n[/itex] could this integral be approximated as
[tex]
\int_{\text{all space}} |\psi_{n}(x)|^2 |\psi_{m}(x)|^2 dx \approx \left( \int |\psi_{n}(x)|^2 dx \right) \left( \int |\psi_{m}(x)|^2 dx \right) =1
[/tex]
I have tried integrating by parts and waded through a couple of identities but I haven't been able to make much progress. Any ideas would be appreciated.

Thanks,
 
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  • #2
jfy4,
I do not know how to calculate your integral, but the approximation you have indicated cannot work for any n,m because the right-hand side of the equality has different dimensions.
 
  • #3
jfy4, I don't see any physical relevance of the integrals you are interested in
 
  • #4
Jano L. said:
jfy4,
I do not know how to calculate your integral, but the approximation you have indicated cannot work for any n,m because the right-hand side of the equality has different dimensions.
Good point, thanks.
 
  • #5
tom.stoer said:
jfy4, I don't see any physical relevance of the integrals you are interested in

now, neither do I...
 
  • #6
Last edited by a moderator:

Related to There is no scientific content in this webpage title.

What is a probability density integral?

A probability density integral is a mathematical concept used to calculate the probability that a continuous random variable falls within a certain range of values. It is represented by the integral of the probability density function over a specific interval.

How is a probability density integral different from a regular integral?

A regular integral is used to find the area under a curve, while a probability density integral is used to find the probability under a curve. In the case of a probability density integral, the total area under the curve is equal to 1, representing the total probability of all possible outcomes.

What is the relationship between a probability density integral and a probability density function?

A probability density integral is calculated by integrating the probability density function over a specific interval. The probability density function is a mathematical function that describes the probability distribution of a continuous random variable. It is used to calculate the probability of a random variable falling within a certain range of values.

What is the importance of using a probability density integral in probability theory?

Probability density integrals are important in probability theory because they allow us to calculate the probability of continuous random variables, which are variables that can take on any value within a certain range. This is essential for understanding and predicting real-world phenomena, such as stock prices, weather patterns, or population growth.

How do you interpret the result of a probability density integral?

The result of a probability density integral represents the probability that a random variable falls within a specific range of values. It can be interpreted as the likelihood of a particular outcome occurring. The higher the value of the integral, the more likely it is for the random variable to fall within that range of values.

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