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The Onsager fluctuation regression theorem

[ "article:topic", "Author tag:Tuckerman", "showtoc:no" ]
  • Page ID
    5303
  • Suppose that \(F_e (t) \) is of the form

    \[ F_e(t) = F_0e^{\epsilon t}\theta(-t) \]


    which adiabatically induces a fluctuation in the system for \(\underline {t < 0 } \) and the lets the system evolve in time according to the unperturbed Hamiltonian for \( \underline {t > 0 } \). How will the induced fluctuation evolve in time? Combining the kubo transform relation with the linear response result for \(\langle B(t)\rangle \), we find that

    \(\langle B(t)\rangle \) $\textstyle =$ data-cke-saved-style =$ data-cke-saved-style =$ data-cke-saved-style =$ data-cke-saved-style =$ data-cke-saved-style =$

    \(\int_{-\infty}^0ds e^{\epsilon s}\int_0^{\beta} d\lambda\langle \dot{B}(-i\hbar\lambda)B(t-s)\rangle _0 \)

     
      $\textstyle =$ data-cke-saved-style =$ data-cke-saved-style =$ data-cke-saved-style =$ data-cke-saved-style =$ data-cke-saved-style =$

    \(-e^{\epsilon t}\int_0^{\beta}d\lambda \int_t^{\infty}du e^{-\epsilon u}{d \over du}\langle B(-i\hbar\lambda)B(u)\rangle _0 \)

     


    where the change of variables \(\underline {u=t-s }\) has been made. Taking the limit \(\epsilon\rightarrow 0 \), and performing the integral over \(u \), we find

    \[ \langle B(t)\rangle = -\int_0^{\beta}d\lambda\left[\langle B (- i\hbar \lambda) B (\infty )\rangle _0 -\langle B(-i\hbar\lambda)B(t)\rangle _0\right]\]


    Since we assumed that \( \langle B\rangle _0 = 0 \), we have \( \langle B(-i\hbar\lambda)B(\infty)\rangle _0 =\langle B(-i\hbar\lambda)\rangle _0\langle B(\infty)\rangle _0 = 0 \). Thus, dividing by \(\langle B(0)\rangle \) , we find

    \[ {\langle B(t)\rangle \over \langle B(0)\rangle } = {\int_0^{\beta} d \lambda B (-i\hbar \lambda) B (t) \rangle _0 \over \int_0^{\beta} d \lambda B (-i\hbar \lambda) B (0) \rangle _0} \rightarrow _{\hbar \rightarrow 0 } {\langle B(0)B(t)\rangle _0 \over \langle B(0)^2 \rangle _0} \]


    Thus at long times in the classical limit, the fluctuations decay to 0, indicting a complete regression or suppression of the induced fluctuation:

    \[ {\langle B(t)\rangle \over \langle B(0) \rangle }\rightarrow 0 \]