Proof of the general form of the equipartition theorem

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SUMMARY

The discussion focuses on proving the equipartition theorem for a Hamiltonian system defined as 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}). Participants aim to demonstrate 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. The conversation reveals confusion regarding the application of probability distribution functions and the calculation of averages, particularly in relation to the integral forms provided.

PREREQUISITES
  • Understanding of Hamiltonian mechanics
  • Familiarity with statistical mechanics concepts
  • Knowledge of probability distribution functions
  • Experience with integrals in multiple dimensions
NEXT STEPS
  • Study the derivation of the equipartition theorem in classical mechanics
  • Learn about the role of the partition function Z_N in statistical mechanics
  • Explore the calculation of averages using probability distribution functions
  • Investigate the implications of the Boltzmann factor P(q,p)=\frac{exp[-\beta H(q,p)]}{Z_N}
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Students and researchers in physics, particularly those studying thermodynamics and statistical mechanics, as well as anyone seeking to understand the equipartition theorem and its applications in Hamiltonian systems.

Homo Novus
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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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