Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1]; Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in Physics [2]; moreover, Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing and in artificial intelligence for defining neural networks.

翻译：将实验数据应用到曲线上是一种常见的信号处理技术,用于提取数据特征并确定变量之间的关系。我们通常期望曲线符合某些分析功能,然后将数据适合用于估算一个函数的未知参数。在数据匹配的分析功能中,高斯函数因其在许多科学和工程领域的广泛应用而是最广泛使用的一种功能。仅举几个例子,高斯函数在统计信号处理和分析中非常受欢迎,这得益于中心参数[1];高斯函数经常出现在量子声振荡器、量子场理论、光学、激光以及物理中许多其他理论和模型[2];此外,高斯函数在化学中广泛应用,用于描述分子轨道,用于图像处理的计算机科学和用于确定神经网络的人工智能。