Proof of the general form of the equipartition theorem

AI Thread Summary
The discussion revolves around proving the equipartition theorem for a Hamiltonian system with specified forms of kinetic and potential energy. Participants express confusion about how to manipulate the provided equations and integrate the probability distribution function to derive the averages of kinetic and potential energy terms. There is a focus on understanding how to compute the average value of functions related to the system's coordinates and momenta, specifically addressing the calculation of averages like \(\overline{a_jp_j^2}\) and \(\overline{b_jq_j^2}\). The conversation highlights the challenges in applying statistical mechanics principles and integrating over phase space. Overall, the thread emphasizes the need for clarity in deriving results from the Hamiltonian framework.
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Homework Statement



For a Hamiltonian of the form H=\sum_{j=1}^m a_j p_j^2 + \sum_{j=1}^n b_j q_j^2 + H'(q_{n+1}, \dots, q_{sN}, p_{m+1}, \dots, p_{sN}) (for a system with n coordinates and m momenta, s degrees of freedom and N particles), show that \overline{a_jp_j^2}=\frac{kT}{2}, for j=1,...,m and \overline{b_jq_j^2}=\frac{kT}{2}, for j=1,...,n.

Homework Equations



P(q,p)=\frac{exp[-\beta H(q,p)]}{Z_N}
Z_N=\frac{1}{N!h^{sN}}\int \int exp[-\beta H(q,p)]\,d^{sN}q\,d^{sN}p
P_x(x_1,...,x_N)dx_1...dx_n = P_y(y_1(x_1,...,x_N),...,y_N(x_1,...,x_N)) \left|J\left(\frac{y_1,...,y_N}{x_1,...,x_N}\right)\right| dx_1...dx_N

Note that \beta=\frac{1}{kT}.

The Attempt at a Solution



I honestly don't really know where to go with this... I've tried plugging stuff in, but I'm really at a loss. Any help would be greatly appreciated. Thanks so much in advance!
 
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Hello, Homo Novus.
Homo Novus said:

The Attempt at a Solution



... I've tried plugging stuff in, but I'm really at a loss.

Can you elaborate on what you did when you were plugging stuff in?
 
I tried messing around with the formulas, but honestly I don't really know what I'm doing. I tried to find P(a_jp_j^2,b_jq_j^2) from P(q,p), but I don't know what to do with the d^{sN}q\,d^{sN}p left over... and then, assuming I do manage to find the correct distribution formula, I don't know how to find the average.
 
Suppose you have a probability distribution function ##p(x)## for some continuous variable x. And suppose you have some function of x, ##f(x)##. Do you know how to find the average value of ##f(x)##: ##\overline{f(x)}##? [Edit: In particular, how would you find ##\overline{x^2}##?]
 
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