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.

中文翻译：

---

谱线形状的快速近似，以优化滤波瑞利散射实验

测量散射光是许多基于激光的气体诊断技术的核心，例如相干反斯托克斯拉曼光谱 (CARS) 和滤波瑞利散射 (FRS)。为了使用这种技术进行定量测量，散射光谱线形的计算模型是必要的。虽然准确，但这些模型通常对计算要求很高，因此不能用于计算速度很重要的情况。为了克服这个问题，可以改为使用这些谱线形状模型的近似值。在本文中，我们开发了一种称为支持向量谱近似 (SVSA) 的方法。该方法使用机器学习来创建任何现有谱线形状模型的有效且准确的近似值。SVSA 框架通过允许在任意流态下计算谱线形状的有效近似来改进现有方法。我们证明了 SVSA 在近似相干和自发 Rayleigh-Brillioun 光谱方面的功效。我们还表明 SVSA 将模拟滤波瑞利散射实验的计算成本降低了 300 倍。