A Proof of Classical Fluctuation-Dissipation Theorem

AI Thread Summary
The discussion centers on the proof of the classical fluctuation-dissipation theorem (FDT) and the challenges faced in deriving the expected value of position over time, specifically $$\langle x(t) \rangle$$. The initial steps leading to the probability density function and its perturbation effects are acknowledged as correct, but difficulties arise in evaluating the integral involving the conditional probability $$P(x,t|x',0)$$ and the initial distribution. There is also skepticism regarding the FDT's form presented in the Wikipedia article, particularly concerning the relationship between the autocorrelation function and the Fourier transform. The participant expresses a desire for alternative proofs or guidance on resolving the integral issue. Overall, the conversation highlights both the mathematical complexities involved and the need for clarification on the FDT's implications.
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Found a proof of the classical version of the fluctuation-dissipation theorem, couldn't figure out one step. Involves classical transition probabilities for a stochastic, dissipative system. How does one evaluate the integral ##\int dx \text{ } x \int dx' P(x,t|x',0) W_0 (x') x'(0)##.
Sorry if there's latex errors. My internet connection is so bad I can't preview.

Here's the wikipedia proof I'm referring to. I'm fine with the steps up to $$W(x,0) = W_0 (x) [1 + \beta f_0 (x(0) - \langle x \rangle_0) ]$$ where ##W(x,t)## is the probability density of finding the system at state ##x## at time ##t##, ##f_0## is the magnitude of a perturbation ##f_0 << kT## on the Hamiltonian H of the form ##H = H_0 - x f_0 \Theta (-t) ## where ##\Theta (t)## is the heaviside step function, ##\langle \cdot \rangle_0## is expectation value in the unperturbed equilibrium distribution (called ##W_0 (x)##), and where ##\beta = \frac{1}{kT}##.

I was able to reproduce the approximate, first order perturbation expression for ##W(x,0)## just fine using taylor/binomial series. It's the next step, using this distribution to prove $$\langle x(t) \rangle = \langle x \rangle_0 + \beta f_0 A(t)$$ where ##A(t) = \langle [x(t) - \langle x \rangle_0][x(0) - \langle x \rangle_0] \rangle_0## is the autocorrelation function.

I'm confident that the general procedure is to write $$\langle x(t) \rangle = \int dx \text{ } x \int dx' P(x,t|x',0) W(x',0) $$ where ##P(x,t|x',0)## is the conditional probability for state x at time t given the system is in state x' at time 0. I can see why ##\int dx' P(x,t|x',0) W_0 (x') = W_0(x)## since ##W_0## is an equilibrium distribution. I also have a hunch that ##\lim_{t \rightarrow \infty} \int dx' P(x,t|x',0) W(x',0) = W_0(x)## for any initial distribution since it's a dissipative system and will reach equilibrium eventually.

However, given all this, what I get is $$\langle x(t) \rangle = \int dx \text{ } x \int dx' P(x,t|x',0) W_0 (x') [1 + \beta f_0 (x'(0) - \langle x \rangle_0) ] = \langle x \rangle_0 + \beta f_0 \left[ \int dx \text{ } x \int dx' P(x,t|x',0) W_0 (x') x'(0) - \int dx \text{ } x W_0(x) \langle x \rangle_0 \right] = \langle x \rangle_0 + \beta f_0 \left[ \int dx \text{ } x \int dx' P(x,t|x',0) W_0 (x') x'(0) - \langle x \rangle_0^2 \right]$$ It's the integral $$I = \int dx \text{ } x \int dx' P(x,t|x',0) W_0 (x') x'(0)$$ that gets me. I have no idea how to evaluate this, because the thing that ##P(x,t|x',0)## is multiplied by isn't a probability distribution. Can anyone point me in the right direction?

Alternatively, if you have a different concise proof, I'm all ears. I was interested in this proof because it's (relatively) short and might be easier to remember.
 
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Update: I'm having serious doubts with the form of the FDT stated in the wikipedia article. They claim $$S_x (\omega) = \frac{2kT}{\omega} \mathrm{Im}[\chi(\omega)]$$ However, for the vanilla Langevin equation $$m\ddot{x} = -\gamma \dot{x} + \eta$$ I am fairly confident that $$\lim_{t \rightarrow \infty} \langle \dot{x}(t+\tau) \dot{x}(t) \rangle \propto e^{-\gamma \tau}$$ In other words $$\lim_{t \rightarrow \infty} \langle \dot{x}(t+\tau) \dot{x}(t) \rangle \propto \chi(\tau)$$ and that's inconsistent with the wikipedia article's claim since the Fourier transform of ##e^{-\gamma \tau}## has non-zero real part.
 
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