Taylor Series Expansion Confusion

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SUMMARY

The discussion centers on the application of the Taylor series expansion in deriving the Boltzmann distribution using a canonical ensemble in thermodynamics. Participants clarify that the differentiation is performed with respect to energy (E), not epsilon (ε), and emphasize the importance of using the Taylor series rather than the Maclaurin series for accurate representation. The final conclusion confirms that the logarithm of the number of microstates, ln Ω(E), can be expressed as a Taylor series around E, leading to the equation ln Ω(E - ε) = ln Ω(E) - (d ln Ω(E)/dE)ε + O(ε²).

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  • Understanding of Taylor series and Maclaurin series
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  • Knowledge of differentiation techniques in calculus
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laser1
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Homework Statement
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Relevant Equations
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Screenshot_1.png

For context, this is when deriving the Boltzmann distribution by using a canonical ensemble (thermodynamics).

omega is a function to represent number of microstates. According to wikipedia...

Screenshot_2.png

is the first order expansion around 0 (Maclaurin series).

My confusion: What are even differentiating with respect to? It seems like we are differentiating with respect to E, but then the epsilon looks like the "x" in the Maclaurin formula. I have more questions, but I'll leave it at this to avoid confusion for now. Thanks!
 
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Let's analyze it from behind. The Taylor series of a function ##f(x)## at a point ##a## is given by
$$
T(f,x,a)=\sum_{n\ge 0} \dfrac{f^{(n)}(a)}{n!}(x-a)^n=f(a)+f'(a)(x-a)+O((x-a)^2)
$$
For ##f(a)## we have ##f(a)=\ln \Omega(E),## i.e. ##a=E.## Then
$$
f'(a)(x-a)=f'(E)(x-E)=\dfrac{d\ln \Omega(E)}{dx}(x-E)
$$
which means the variable is ##x=-\varepsilon + E## and the second term of the series becomes
$$
f'(E)\cdot((-\varepsilon+E)-E)=\dfrac{d\ln \Omega(E)}{d(E-\varepsilon)}(-\varepsilon+E-E)=-\dfrac{d\ln \Omega(E)}{d(E-\varepsilon)}\varepsilon
$$
We have ##d(E-\varepsilon)=dE - d\varepsilon## and with ##\varepsilon## being a constant we have ##d\varepsilon = 0.##
Proof:
\begin{align*}
\dfrac{df(E)}{d(E-\varepsilon)}&=\dfrac{df((E-\varepsilon)+\varepsilon)}{d(E-\varepsilon)}=\dfrac{df(y+\varepsilon)}{dy}\\[6pt]
&=\dfrac{df(y+\varepsilon)}{d(y+\varepsilon)}\cdot \underbrace{\dfrac{d(y+\varepsilon)}{dy}}_{=1}\\[6pt]
&=\dfrac{df(E)}{dE}
\end{align*}
In total we get
$$
\ln \Omega (E-\varepsilon)=\ln \Omega(E)- \dfrac{d\ln \Omega(E)}{dE}\varepsilon + O(\varepsilon^2)
$$

laser1 said:
I have more questions, ...
Which?
 
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laser1 said:
According to wikipedia...

View attachment 351220
is the first order expansion around 0 (Maclaurin series).

It looks like they are really expanding around ##E##, not around 0. The function and derivative are evaluated at ##E##. The Maclaurin series is too specialized (centered at ##x_0=0##) to give a good match to your equation. You should compare it to the more general Taylor series. ##f(a)+\frac {f'(a)}{1!}(x-a)+...##. Then ##E=a##, ##E-\epsilon=x##, and ##-\epsilon=x-a##.
laser1 said:
My confusion: What are even differentiating with respect to? It seems like we are differentiating with respect to E,
That is correct.
 
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@fresh_42 Thank you, that makes sense. I also tried differentiating it with respect to epsilon around 0 and got this, but I am stuck at this step:
WhatsApp Image 2024-09-18 at 08.21.10.jpeg
 
laser1 said:
@fresh_42 Thank you, that makes sense. I also tried differentiating it with respect to epsilon around 0 and got this, but I am stuck at this step:

Apply the chain rule.
 
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pasmith said:
Apply the chain rule.
WhatsApp Image 2024-09-21 at 21.30.38.jpeg


I did it a few days ago and was happy, but upon closer examination: I suspect dE/d(epsilon) = -1 only if the total energy of the ensemble is a constant.

Any idea where I have gone wrong with my second method?
 
laser1 said:
View attachment 351397

I did it a few days ago and was happy, but upon closer examination: I suspect dE/d(epsilon) = -1 only if the total energy of the ensemble is a constant.

Any idea where I have gone wrong with my second method?
I don't think that you made a mistake.
\begin{align*}
\left.\dfrac{d\Omega(E-\varepsilon)}{d\varepsilon}\right|_{\varepsilon=0}&=\left.\dfrac{d\Omega(E-\varepsilon)}{d(E-\varepsilon)}\right|_{E-\varepsilon=E}\cdot \left.\dfrac{d(E-\varepsilon)}{d\varepsilon}\right|_{\varepsilon=0}\\
&=\left.\dfrac{d\Omega(E-\varepsilon)}{d(E-\varepsilon)}\right|_{E-\varepsilon=E}\cdot\left\{\underbrace{\left.\dfrac{dE}{d\varepsilon}\right|_{\varepsilon=0}}_{=0}+\underbrace{\left.\dfrac{d(-\varepsilon)}{d\varepsilon}\right|_{\varepsilon=0}}_{=-1}\right\}\\
&=-\left.\dfrac{d\Omega(E-\varepsilon)}{d(E-\varepsilon)}\right|_{E-\varepsilon=E}
\end{align*}
The trick is now that it doesn't matter how you call your variables. We simply have
$$
-\left.\dfrac{d\Omega(E-\varepsilon)}{d(E-\varepsilon)}\right|_{E-\varepsilon=E}=-\left.\dfrac{d\Omega(x)}{dx}\right|_{x=E}=-\left.\dfrac{d\Omega(E)}{dE}\right|_{E}=-\dfrac{d\Omega(E)}{dE}
$$
 
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