How is an autocorrelation function computed? (Dynamic Light Scattering)

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SUMMARY

The computation of an autocorrelation function in Dynamic Light Scattering (DLS) involves the mathematical expression G(τ) = ⟨I(t)I(t+τ)⟩, where I(t) represents the scattered intensity collected by a single detector. The digital correlator processes this signal by taking the Fast Fourier Transform (FFT) of the intensity data, multiplying the resulting spectra, and then applying the inverse FFT to obtain the autocorrelation function. This method effectively filters the signal by itself, allowing for the analysis of temporal correlations in the scattered light intensity.

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  • Understanding of Dynamic Light Scattering (DLS)
  • Familiarity with Fast Fourier Transform (FFT) techniques
  • Knowledge of digital signal processing concepts
  • Basic mathematical concepts related to correlation functions
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Salmone
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In an experiment of Dynamic Light Scattering, how is an autocorrelation like the one in the image computed?

dls-theory-figure-1-500.png

Mathematically a correlation function can be written as ##G(\tau)=\langle I(t)I(t+\tau) \rangle##, in an experiment like the one I mentioned the scattered intensity light is collected by a single detector, then the signal is sent to a digital correlator which computes the correlation function. How this process works? Once I have a signal from the detector, what does the correlator do? Does it multiply the intensity at time ##t## with the same intensity at time ##t+\tau## simply? How is the average implemented? By recording with the detector the same scattered intensity multiple times? Can you explain very generally how a digital correlator works?
 
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Salmone said:
Can you explain very generally how a digital correlator works?
A correlator would take the FFT of the two signals, multiply those two spectra, then inverse FFT. In effect, filtering a signal by another signal.

I expect autocorrelation could be performed by taking the FFT of the signal, squaring the vectors of the resulting spectrum, then computing the inverse FFT. In effect, filtering a signal by itself.
 

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