Expansion in a Small Parameter

In summary: Can someone tell me why this is?In summary, when an expression is derived using an "expansion in small parameters," it is generally a method for calculating a probability. However, in the case of neutrino oscillation probabilities, only functions of the small parameters are left after a Taylor expansion. This may be due to the fact that the approximation is based on the inner product of the states, which does not involve multiplication by the small parameter.
  • #1
Dahaka14
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When an expression is derived using an "expansion in small parameters," what do they generally mean? I am not too familiar with this term, and the only thing I can think of are Taylor expansions. I have seen expressions that contain functions of these parameters, and after the expansion in small parameters, the answer still contains functions of the parameters, but no nth power terms of the parameters (the [tex](x-x_{0})^{n}[/tex] terms from Taylor expansions). Can someone help me understand how this is done? If needbe, I can give the examples I am referring to. I am posting this in this particular forum since the example has to do with neutrino oscillations. Let me know if I should post somewhere else.
 
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  • #2
expansion in small parameters are general, this is not anything particular for neutrino oscillations, but yes one performs a Taylor expansion anyhow...
 
  • #3
I know it's a general method, but the example that uses it and confuses me is in neutrino oscillation probabilities. In a paper, specifically [A. Cervera et al., Nucl. Phys. B579, 17 (2000)] and other papers that discuss probabilities which include CP-violation and matter effects, the probability for the electron neutrino to antineutrino oscillation, expanded to second order in the small parameters [tex]\theta_{13},\Delta_{12}/\Delta_{13}~\text{and}~\Delta_{12}L[/tex], is
[tex]P_{\nu_{e}\rightarrow\nu_{\mu}}=\sin^{2}\theta_{23}\sin^{2}2\theta_{13}\sin^{2}\left(\frac{\Delta_{13}L}{2}\right)+\cos^{2}\theta_{23}\sin^{2}2\theta_{12}\sin^{2}\left(\Delta_{12}L}{2}\right)+\tilde{J}\cos\left(\pm\delta-\frac{\Delta_{13}L}{2}\right)\frac{\Delta_{12}L}{2}\sin\left(\frac{\Delta_{13}L}{2}\right)[/tex]
where [tex]\tilde{J}\equiv\cos\theta_{13}\sin2\theta_{12}\sin2\theta_{23}\sin2\theta_{13}[/tex].
How is it that only functions of these parameters are left after Taylor expansion, and no linear or quadratic terms are left of say [tex]\theta_{13}[/tex]?
 
  • #4
Dahaka14 said:
I know it's a general method, but the example that uses it and confuses me is in neutrino oscillation probabilities. In a paper, specifically [A. Cervera et al., Nucl. Phys. B579, 17 (2000)] and other papers that discuss probabilities which include CP-violation and matter effects, the probability for the electron neutrino to antineutrino oscillation, expanded to second order in the small parameters [tex]\theta_{13},\Delta_{12}/\Delta_{13}~\text{and}~\Delta_{12}L[/tex], is
[tex]P_{\nu_{e}\rightarrow\nu_{\mu}}=\sin^{2}\theta_{23}\sin^{2}2\theta_{13}\sin^{2}\left(\frac{\Delta_{13}L}{2}\right)+\cos^{2}\theta_{23}\sin^{2}2\theta_{12}\sin^{2}\left(\Delta_{12}L}{2}\right)+\tilde{J}\cos\left(\pm\delta-\frac{\Delta_{13}L}{2}\right)\frac{\Delta_{12}L}{2}\sin\left(\frac{\Delta_{13}L}{2}\right)[/tex]
where [tex]\tilde{J}\equiv\cos\theta_{13}\sin2\theta_{12}\sin2\theta_{23}\sin2\theta_{13}[/tex].
How is it that only functions of these parameters are left after Taylor expansion, and no linear or quadratic terms are left of say [tex]\theta_{13}[/tex]?

Correct me if I am wrong, but is the first term in your equation for P, not quadratic in [tex]\theta_{13}[/tex]?

Have you worked through the derivation yourself? The devil is always in the details. Unless someone familiar with the problem can argue about the probability without doing the derivation.
 
  • #5
I double checked the expression, and it should be quadratic. Take a look at equation 7 on page 7 in the arxiv version http://arxiv.org/PS_cache/hep-ph/pdf/0002/0002108v3.pdf" . I took the inner product of the states and took absolute square, which has given me a large mess of terms. When I try to expand parts in [tex]\theta_{13}[/tex], I always end up with factors of [tex]\theta_{13}[/tex] and [tex]\sin^{2}2\theta_{13}[/tex], but there only appear to be factors of [tex]\sin^{2}2\theta_{13}[/tex] in their answer. There are other papers that perform similar approximations, and yet they never end up with things being multiplied by [tex]\theta_{13}[/tex].
 
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FAQ: Expansion in a Small Parameter

1. What is expansion in a small parameter?

Expansion in a small parameter is a mathematical technique used to approximate complicated functions by breaking them down into simpler components. It involves expanding a function into a series of terms, where each term is multiplied by a small parameter that is assumed to be close to zero.

2. Why is expansion in a small parameter useful?

This technique is useful because it allows us to approximate complex functions with a much simpler form, making them easier to analyze and manipulate. It is commonly used in physics, engineering, and other fields to solve problems that would otherwise be too difficult to solve directly.

3. What is an example of expansion in a small parameter?

An example would be using Taylor series expansion to approximate a function in terms of a small variable, such as expanding sin(x) around x=0 into x and higher order terms.

4. What are the limitations of expansion in a small parameter?

One limitation is that it only provides an approximation of the function, which may not be accurate for large values of the small parameter. Additionally, it may not be applicable to all functions, as some may not have a convergent series expansion.

5. How is expansion in a small parameter related to perturbation theory?

Perturbation theory is a specific application of expansion in a small parameter, where the small parameter represents a small deviation from a known solution. It is commonly used to approximate solutions to equations that cannot be solved exactly, such as in quantum mechanics and celestial mechanics.

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