Spectral-diffusion frequency-increment variance
ID: spectral-diffusion-frequency-increment-variance
The spectral-diffusion frequency-increment variance quantifies how much a transition frequency changes between two observation times. For a real stationary process with mean and autocovariance function ,The mean increment is zero under these assumptions, so its second moment is also its variance. Expanding the square proves the identity: the two squared-frequency deviations each contribute and their cross term contributes .
If , thenFor an original example with and , and the root-mean-square increment is about . The single-time frequency spread remains ; temporal decorrelation does not require the stationary distribution to broaden.
The long-time value assumes that the autocovariance function tends to zero. It is not a universal upper bound. For a random-phase sinusoid with phase uniform over a full cycle,At half a cycle, the frequencies are anticorrelated and ; at a complete cycle the increment vanishes. Thus a nonmonotone increment statistic can reflect recurring frequency memory.
For nonstationary observations , the general identity instead isDrift of the mean must not be silently assigned to stationary spectral diffusion. Likewise, under an explicit model of independent zero-mean endpoint measurement errors with variance , the observed second moment gains . This spectral frequency-increment measurement noise term changes when the endpoint errors are correlated.
Primary two-dimensional infrared spectroscopy work uses controlled frequency labeling and later readout to constrain frequency memory.[ref]
Frequency-frequency correlation functions and apodization in two-dimensional infrared vibrational echo spectroscopy: A new approach by Kyungwon Kwak and others. Introduction, pulse delays and frequency-frequency correlation function
The identity above is a model relation; a measured 2D IR center-line slope needs the stated response assumptions before it supplies . New to topics? Read the docs here!