Fourier transform spectroscopy measures an interferogram as a function of optical path length difference. Its Fourier transform recovers the spectrum. An imaging implementation obtains an interferogram at every detector pixel, but a scanned cube is not simultaneous at all wavelengths.
Multiplying a finite interferogram by a smooth window reduces sharp-edge artifacts. In the recovered radiation spectrum this convolves the true spectral density with the window's Fourier transform. Smoother windows generally lower sidelobes while broadening the main lobe, so a resolution figure must specify the window and criterion. Apodization does not recover unmeasured long-delay information.[ref]
Introduction to Fourier Transform Spectroscopy by Julius Cohen. chapter 14, printed pp.57–62, and chapter 15: sidelobe/resolution tradeoff
For equally intense spectral lines at , sum their delayed self-correlations as a geometric series. The result isThe interferometric visibility revives every . There are zeros between adjacent revivals, at for . The removable singularity at a revival has unit modulus. Revival spacing fixes line spacing; the internal zero count fixes under the equal-intensity model. The interferogram alone does not establish a fixed relative phase between the source lines or prove the emission is a coherent pulse train.
A Michelson interferometer mirror displacement changes round-trip optical path difference by , giving delay . An interferogram recorded over a finite delay range is multiplied by a window; the recovered spectrum is the true spectrum convolved with that window's Fourier transform. Its characteristic frequency resolution scales inversely with delay range. Consequently a mirror travel range gives and . Numerical width factors depend on the scan definition, window and resolution criterion; the inverse-travel scaling does not.
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Fourier-transform spectroscopy (FTS) is an analytical technique used to obtain the spectrum of a substance by measuring the intensity of light as a function of wavelength or frequency. The core principle of FTS is the application of Fourier transform mathematics to process the data collected from spectroscopic measurements. Here’s how it works in a nutshell: 1. **Interferometry**: FTS typically employs an interferometer, such as a Michelson interferometer, to split an incoming light beam into two paths.