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Linear response function
Linear response function
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A linear response function describes the input-output relationship of a signal transducer, such as a radio turning electromagnetic waves into music or a neuron turning synaptic input into a response. Because of its many applications in information theory, physics and engineering there exist alternative names for specific linear response functions such as susceptibility, impulse response or impedance; see also transfer function. The concept of a Green's function or fundamental solution of an ordinary differential equation is closely related.

Mathematical definition

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Denote the input of a system by (e.g. a force), and the response of the system by (e.g. a position). Generally, the value of will depend not only on the present value of , but also on past values. Approximately is a weighted sum of the previous values of , with the weights given by the linear response function :

The explicit term on the right-hand side is the leading order term of a Volterra expansion for the full nonlinear response. If the system in question is highly non-linear, higher order terms in the expansion, denoted by the dots, become important and the signal transducer cannot adequately be described just by its linear response function.

The complex-valued Fourier transform of the linear response function is very useful as it describes the output of the system if the input is a sine wave with frequency . The output reads

with amplitude gain and phase shift .

Example

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Consider a damped harmonic oscillator with input given by an external driving force ,

The complex-valued Fourier transform of the linear response function is given by

The amplitude gain is given by the magnitude of the complex number and the phase shift by the arctan of the imaginary part of the function divided by the real one.

From this representation, we see that for small the Fourier transform of the linear response function yields a pronounced maximum ("Resonance") at the frequency . The linear response function for a harmonic oscillator is mathematically identical to that of an RLC circuit. The width of the maximum, typically is much smaller than so that the Quality factor can be extremely large.

Kubo formula

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The exposition of linear response theory, in the context of quantum statistics, can be found in a paper by Ryogo Kubo.[1] This defines particularly the Kubo formula, which considers the general case that the "force" h(t) is a perturbation of the basic operator of the system, the Hamiltonian, where corresponds to a measurable quantity as input, while the output x(t) is the perturbation of the thermal expectation of another measurable quantity . The Kubo formula then defines the quantum-statistical calculation of the susceptibility by a general formula involving only the mentioned operators.

As a consequence of the principle of causality the complex-valued function has poles only in the lower half-plane. This leads to the Kramers–Kronig relations, which relates the real and the imaginary parts of by integration. The simplest example is once more the damped harmonic oscillator.[2]

Nonequilibrium linear response formula

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Linear response theory has versions for nonequilibrium processes for open systems where there is no detailed balance but a steady driving or agitation is applied. A small perturbation of these driven or active systems gives rise to a response in violation with the equilibrium expressions. A possible methodology proceeds via path-space ensembles where the probabilities of trajectories are evaluated; see e.g.[3] The resulting response formulae have an entropic part (similar to the detailed balance case) and a frenetic part. The latter involves the correlation of the (excess) frenesy (due to the perturbation) with the observable. In detailed balance, the two contributions merge and reproduce the Kubo and Green-Kubo formulae. Out of detailed balance, the frenetic contribution is responsible for the possibility of negative heat capacities and mobilities, and they do not longer measure a fluctuation, e.g. in terms of a variance of the energy or of the current.

See also

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References

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from Grokipedia
The linear response function, also known as the response function or susceptibility, is a in and quantum physics that describes the linear change in the expectation value of an in a subjected to a weak, time-dependent external perturbation. It quantifies how the system deviates from equilibrium, capturing relationships between the perturbation and the induced response while ensuring principles like causality and are satisfied through its retarded nature. This framework assumes the perturbation is small enough that higher-order effects are negligible, allowing the response to be proportional to the perturbation's . Developed primarily in the mid-20th century, linear response theory was formalized by Ryogo Kubo in his seminal 1957 paper, which provided a general statistical-mechanical basis for calculating irreversible processes and transport coefficients from equilibrium correlation functions. Kubo's approach bridges microscopic dynamics to macroscopic phenomena, showing that the response is directly tied to fluctuations in the unperturbed system via the fluctuation-dissipation theorem. In , the theory applies to systems at finite temperatures, using the to average over equilibrium states. The cornerstone of the theory is the Kubo formula, which expresses the response function χAB(tt)\chi_{AB}(t - t') between observables AA and BB as χAB(tt)=iθ(tt)[A(t),B(t)]0\chi_{AB}(t - t') = -i \theta(t - t') \langle [A(t), B(t')] \rangle_0, where θ\theta is the enforcing , and the brackets denote the equilibrium average of the in the . In frequency space, this becomes a complex function whose real part relates to reactive effects and imaginary part to dissipation, enabling computations via Fourier transforms of correlation functions. For classical systems, an analogous replaces the . Applications of linear response functions span , including the calculation of electrical conductivity σ(ω)=e2iωχjj(ω)\sigma(\omega) = \frac{e^2}{i\omega} \chi_{jj}(\omega) from current-current correlations, responses in materials, and magnetic susceptibilities. It is also essential for interpreting experiments like optical absorption, neutron scattering, and angle-resolved photoemission spectroscopy (ARPES), where external probes reveal intrinsic material properties. Extensions to nonequilibrium and open quantum systems further broaden its utility in modern fields like and nanoscale transport.

Basic Concepts

Definition

Linear response theory provides a fundamental framework in and physics for predicting the behavior of physical systems subjected to weak external perturbations, such as electric or magnetic fields. It assumes that the system's response, measured through changes in expectation values of observables, is linearly proportional to the strength of the perturbation, which is valid for small disturbances that do not significantly alter the equilibrium state. The core concept is encapsulated in the response function χAB(tt)\chi_{AB}(t - t'), which quantifies the change in the expectation value of AA at time tt, denoted δA(t)\delta \langle A(t) \rangle, due to a perturbation δhB(t)\delta h_B(t') in the conjugate field hBh_B at an earlier or simultaneous time tt'. This relationship is expressed as δA(t)=χAB(tt)δhB(t)dt,\delta \langle A(t) \rangle = \int_{-\infty}^{\infty} \chi_{AB}(t - t') \, \delta h_B(t') \, dt', where the integral accounts for the superposition of responses from all perturbation times, assuming time-translation invariance in the unperturbed system. A key property of the response function is causality, which dictates that χAB(tt)=0\chi_{AB}(t - t') = 0 for t<tt < t', ensuring that the system's response cannot precede the applied perturbation and thus preserving the directional flow of time in physical processes. This theoretical framework originated in the 1950s within the development of nonequilibrium statistical mechanics, with pivotal contributions from Ryogo Kubo and collaborators, who formalized the connection between linear responses and equilibrium fluctuations.

Linear Approximation

The linear approximation in response theory assumes that the perturbation to the system's Hamiltonian, typically expressed as δH=h(t)B\delta H = -h(t) B where h(t)h(t) is a small external field and BB is an operator coupling to it, is much weaker than the unperturbed Hamiltonian H0H_0, i.e., δHH0\delta H \ll H_0. This allows the system's response to be described by retaining only the first-order term in the perturbation expansion, neglecting higher-order contributions that become negligible under this condition. The change in the expectation value of an observable AA, denoted δA\delta \langle A \rangle, can be expanded around the equilibrium state using a Taylor series in the perturbation strength hh: δAAhh=0δh,\delta \langle A \rangle \approx \left. \frac{\partial \langle A \rangle}{\partial h} \right|_{h=0} \delta h,
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