  Measuring scattered light is central to many laser-based gas diagnostic
techniques, e.g., coherent anti-Stokes Raman spectroscopy (CARS) and filtered
Rayleigh scattering (FRS). To produce quantitative measurements with such
techniques, a computational model of the scattered spectral lineshape is
necessary. While accurate, these models are often quite computationally
demanding and thus cannot be used in situations where computational speed
matters. To overcome this, approximations of these spectral lineshape models
can be used instead. In this paper, we develop a method called support vector
spectrum approximation (SVSA). This method uses machine learning to create
efficient and accurate approximations of any existing spectral lineshape model.
The SVSA framework improves upon existing methods by allowing efficient
approximations of spectral lineshapes to be calculated in arbitrary flow
regimes. We demonstrate the efficacy of SVSA in approximating coherent and
spontaneous Rayleigh-Brillioun spectra. We also show that SVSA reduces the
computational cost of a simulated filtered Rayleigh scattering experiment by a
factor of 300.
