卷积和傅里叶变换:从高斯和洛伦兹函数到q-高斯Tsallis函数

Amelia Carolina Sparavigna
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引用次数: 0

摘要

这里提出了关于Voigt函数的讨论,即高斯函数和洛伦兹函数的卷积,以及lsamvy和q-高斯Tsallis分布。Voigt函数和q-高斯函数可以作为拉曼光谱中的线形来拟合光谱。利用卷积定理,我们可以得到产生Voigt线形的松弛。为了确定控制q-高斯线形的松弛,我们需要使用lsamvy对称分布,因为q-高斯的直接傅里叶变换是一个非常复杂的函数。根据Deng(2010)的工作,q-高斯函数很好地模仿了lsamvy函数。由于lsamvy函数的傅里叶变换是一个拉伸的指数弛豫,我们可以认为同样的机制产生了q-高斯线形。此外,利用q-高斯子的卷积定理,我们可以进一步推广松弛机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolution and Fourier Transform: from Gaussian and Lorentzian Functions to q-Gaussian Tsallis Functions
A discussion is here proposed regarding the Voigt function, that is the convolution of Gaussian and Lorentzian functions, and the Lévy and q-Gaussian Tsallis distributions. The Voigt and q-Gaussian functions can be used as line shapes in Raman spectroscopy for fitting spectra. Using the convolution theorem, we can obtain the relaxations which are producing the Voigt line shape. To determine the relaxation governing the q-Gaussian line shape, we need to use the Lévy symmetric distribution, since the direct Fourier transform of the q-Gaussian is a very complicated function. According to the work by Deng, 2010, the q-Gaussian functions are mimicking the Lévy functions in an excellent manner. Being the Fourier transform of the Lévy function a stretched exponential relaxation, we can argue that the same mechanism is producing the q-Gaussian line shape. Moreover, using the convolution theorem for the q-Gaussians, we can further generalize the relaxation mechanism.
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