Can we use this approximation for k_D<<k_F?

In summary, the conversation discusses the approximation for the difference between two wave vectors, k_F and k, when k_D<<k_F. The Fermi wave vector (k_F) and Debay wave vector (k_D) are defined, and the discussion includes how to neglect certain terms in the equation based on the values of k_D and k_F. The conversation concludes with a clarification of the equation and a note that overall signs do not matter if taking the absolute value.
  • #1
Petar Mali
290
0
For [tex]k_D<<k_F[/tex]

[tex]|\frac{\hbar^2k^2_F}{2m}-\frac{\hbar^2k^2}{2m}|\approx \frac{\hbar^2k_F}{m}|k_F-k|[/tex]

Where [tex]k[/tex] goes from [tex]k-k_D[/tex] to [tex]k+k_D[/tex]

[tex]k_F[/tex] - Fermi wave vector
[tex]k_D[/tex] - Debay wave vector
 
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  • #2
I suppose you mean [itex]k[/itex] goes between [itex]k_F+k_D[/itex] and [itex]k_F-k_D[/itex]?

Yes it is valid. Let's denote [itex]k=k_F+\delta k[/itex], then

[tex]
|\frac{\hbar^2k_F^2}{2m}-\frac{\hbar^2k^2}{2m}|=|\frac{\hbar^2k_F\delta k}{m}+\frac{\hbar^2 \delta k^2}{2m}|
[/tex]

since [itex]\delta k<k_D\ll k_F[/itex] we can neglect the last term (quadratic in [itex]\delta k[/itex] and get

[tex]
|\frac{\hbar^2k_F\delta k}{m}|=\frac{\hbar^2k_F}{m}|k_F-k|
[/itex]
 
  • #3
Thanks a lot! :) Yes from [tex]k_F-k_D[/tex] to [tex]k_F+k_D[/tex].

I think that you have just a little mistake

You must write like

[tex]

|\frac{\hbar^2k_F^2}{2m}-\frac{\hbar^2k^2}{2m}|=|-\frac{\hbar^2k_F\delta k}{m}-\frac{\hbar^2 \delta k^2}{2m}|

[/tex]

To get [tex]C|k_F-k|[/tex] or in case you wrote you will get

[tex]C|k-k_F|[/tex]

You helped me a lot!
 
  • #4
Petar Mali said:
Thanks a lot! :) Yes from [tex]k_F-k_D[/tex] to [tex]k_F+k_D[/tex].

I think that you have just a little mistake

You must write like

[tex]

|\frac{\hbar^2k_F^2}{2m}-\frac{\hbar^2k^2}{2m}|=|-\frac{\hbar^2k_F\delta k}{m}-\frac{\hbar^2 \delta k^2}{2m}|

[/tex]

To get [tex]C|k_F-k|[/tex] or in case you wrote you will get

[tex]C|k-k_F|[/tex]

I assumed that [itex] |\ldots |[/itex] meant taking the absolute value. If this is so then overall signs do not matter.

Anyway, You're welcome.
 
  • #5
Yes! My mistake!
 

1. Can we trust this approximation?

The answer to this question depends on the context of the approximation. It is important to consider the data and methods used to make the approximation, as well as any potential sources of error. Generally, a larger and more diverse dataset and a more rigorous method will result in a more trustworthy approximation.

2. How accurate is this approximation?

The accuracy of an approximation can be evaluated by comparing it to the true value or a more precise approximation. This can be done using statistical measures such as mean absolute error or root mean squared error. It is important to keep in mind that an approximation will never be 100% accurate, but a smaller error indicates a more accurate approximation.

3. Can we improve this approximation?

In most cases, there is room for improvement in an approximation. This can be done by using a different method or adjusting the parameters of the current method. It is also important to consider if more data or a larger sample size can improve the approximation. However, it is important to carefully evaluate any changes to ensure they do not introduce additional errors.

4. What are the limitations of this approximation?

Every approximation has its limitations, and it is important to be aware of them in order to properly interpret the results. Some limitations may include assumptions made in the method, a narrow range of applicability, or potential sources of error. It is important to thoroughly understand the limitations in order to use the approximation appropriately.

5. How can we validate this approximation?

Validation of an approximation involves comparing it to a known or accepted standard. This can be done using a separate dataset or by performing experiments to test the accuracy of the approximation. It is important to validate an approximation before using it for important decisions or conclusions.

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