Proof of the general form of the equipartition theorem

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Homework Help Overview

The discussion revolves around proving a specific aspect of the equipartition theorem within the context of statistical mechanics, particularly focusing on a Hamiltonian that includes both kinetic and potential energy terms for a system with multiple degrees of freedom and particles.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants express uncertainty about how to proceed with the problem, with one mentioning attempts to manipulate formulas and another questioning how to derive the average value of a function from a probability distribution.

Discussion Status

The discussion is ongoing, with participants sharing their struggles and seeking clarification on specific steps in the problem-solving process. Some guidance has been offered regarding the calculation of averages, but no consensus or clear direction has emerged yet.

Contextual Notes

Participants note challenges related to the integration over phase space and the application of probability distribution functions, indicating potential gaps in understanding the foundational concepts necessary for the proof.

Homo Novus
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Homework Statement



For a Hamiltonian of the form [itex]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})[/itex] (for a system with n coordinates and m momenta, s degrees of freedom and N particles), show that [itex]\overline{a_jp_j^2}=\frac{kT}{2}[/itex], for j=1,...,m and [itex]\overline{b_jq_j^2}=\frac{kT}{2}[/itex], for j=1,...,n.

Homework Equations



[itex]P(q,p)=\frac{exp[-\beta H(q,p)]}{Z_N}[/itex]
[itex]Z_N=\frac{1}{N!h^{sN}}\int \int exp[-\beta H(q,p)]\,d^{sN}q\,d^{sN}p[/itex]
[itex]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[/itex]

Note that [itex]\beta=\frac{1}{kT}[/itex].

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 [itex]P(a_jp_j^2,b_jq_j^2)[/itex] from [itex]P(q,p)[/itex], but I don't know what to do with the [itex]d^{sN}q\,d^{sN}p[/itex] 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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