Legendre polynomials in boosted temperature approximation

In summary, the conversation discusses a derivation in S. Weinberg's book "Cosmology" about the temperature of the cosmic microwave background as seen from Earth. The formula used in the book involves Legendre polynomials and is an approximation of the temperature distribution. However, there is a discrepancy between the first term in the book and the calculated term. After further discussion and calculations, it is discovered that the discrepancy was due to an oversight in the ordering of the expansions.
  • #1
jouvelot
53
2
Hi all,

In S. Weinberg's book "Cosmology", there is a derivation of the slightly modified temperature of the cosmic microwave background as seen from the Earth moving w.r.t. a frame at rest in the CMB. On Page 131 (1st printing), an approximation (Formula 2.4.7) is given in terms of Legendre polynomials, but I have a hard time seeing how these polynomials get into the picture. Any hints?

Thanks in advance.

Bye,

Pierre
 
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  • #2
(Mathematical explanation) Most expressions can be expanded in terms of a system of orthogonal functions with coefficients. If the coefficients tend to 0 rapidly enough, then a few terms of the expansion can be used as an approximation to the expression.

I have no knowledge of the particulars you are referring to.
 
  • #3
Hello mathman,

Good point. Since the discussion here in the book is about temperature distribution, it makes sense to use Legendre polynomials to look at possible n-polar terms. A usual limited development factorised using Legendre polynomials yields the formula in the book (well, almost, but I get the idea).

Thanks a lot for your very helpful comment.

Bye,

Pierre
 
  • #4
jouvelot said:
Good point. Since the discussion here in the book is about temperature distribution, it makes sense to use Legendre polynomials to look at possible n-polar terms. A usual limited development factorised using Legendre polynomials yields the formula in the book (well, almost, but I get the idea).

About this "almost" part, the first term in the book is -β2/6 while I find 5β2/6 (there is no mention of a typo here in the Corrections sheet available online). If anyone has an idea... ;)
 
  • #5
jouvelot said:
About this "almost" part, the first term in the book is -β2/6 while I find 5β2/6 (there is no mention of a typo here in the Corrections sheet available online). If anyone has an idea... ;)

Did you subtract ##T## from (2.4.6)? I.e., (2.4.7) = (2.4.6) - ##T##.
 
  • #6
Hello George,

I sure did. Moreover, this gives an additional factor term equals to 1, and not something proportional to β2 that would help explain the discrepancy.

Thanks for having looked into this :)

Pierre
 
  • #7
jouvelot said:
About this "almost" part, the first term in the book is -β2/6 while I find 5β2/6 (there is no mention of a typo here in the Corrections sheet available online). If anyone has an idea... ;)

Okay, now I have done more of a calculation.

The first time I did the calculation, I got ##5 \beta^2/6##.

The second time I got ##- \beta^2/6##.

The first time that I did the calculation, I mistakenly used the expansion ##\gamma = \left( 1 - \beta^2 \right)^{-1/2} = 1 +\beta^2/2 + \ldots## instead of ##\gamma^{-1} = \left( 1 - \beta^2 \right)^{1/2} = 1 -\beta^2/2 + \ldots##.
 
  • #8
George Jones said:
Okay, now I have done more of a calculation.

The first time I did the calculation, I got ##5 \beta^2/6##.

The second time I got ##- \beta^2/6##.

The first time that I did the calculation, I mistakenly used the expansion ##\gamma = \left( 1 - \beta^2 \right)^{-1/2} = 1 +\beta^2/2 + \ldots## instead of ##\gamma^{-1} = \left( 1 - \beta^2 \right)^{1/2} = 1 -\beta^2/2 + \ldots##.

George,

Oh! Great :) I see: one has to be pretty careful about the order with which expansions are performed. I'll sleep better tonight : )

Thanks a lot for your very useful help, and Merry Christmas.

Bye,

Pierre
 
  • #9
Well, ultimately, it was not even a matter of Taylor expansion ordering, but just an oversight on my part. Thanks a lot for spotting it for me, George :)
 

FAQ: Legendre polynomials in boosted temperature approximation

What are Legendre polynomials in boosted temperature approximation?

Legendre polynomials in boosted temperature approximation are mathematical functions used to describe the distribution of energy levels in a system at increased temperatures. They are commonly used in statistical mechanics and thermodynamics to model the behavior of particles at high temperatures.

What is the difference between standard Legendre polynomials and those in boosted temperature approximation?

The standard Legendre polynomials, also known as classical Legendre polynomials, are used to describe the energy levels of a system at low temperatures. In contrast, Legendre polynomials in boosted temperature approximation take into account the effects of increased temperature on the energy levels and provide a more accurate representation of the system's behavior.

How are Legendre polynomials in boosted temperature approximation calculated?

Legendre polynomials in boosted temperature approximation are calculated using a modified form of the standard Legendre polynomials, which incorporates a temperature-dependent factor. This factor takes into account the changes in the energy levels of the system at higher temperatures.

What are the applications of Legendre polynomials in boosted temperature approximation?

Legendre polynomials in boosted temperature approximation have various applications in physics and chemistry, particularly in the study of thermodynamics and statistical mechanics. They are also used in the analysis of phase transitions and in the modeling of complex systems such as polymers and biological molecules.

Are there any limitations to the use of Legendre polynomials in boosted temperature approximation?

While Legendre polynomials in boosted temperature approximation provide a more accurate representation of systems at high temperatures, they may not be suitable for describing extreme conditions or systems with complex interactions. In such cases, more advanced mathematical models may be required to accurately describe the system's behavior.

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