用高斯混合物代替标准星系轮廓
Replacing standard galaxy profiles with mixtures of Gaussians
摘要：
指数分布、de Vaucouleurs分布和Sérsic分布都是简单而成功的星系二维图像拟合模型。在这种拟合中遇到的一个数值问题是模型与望远镜点扩散函数（PSF）的像素渲染和卷积（或相关）;这些操作很慢，并且在小半径处容易出错。在这里，我们利用实现这些模型可以近似到任意精度的混合（线性叠加）的二维高斯（MoG）。MoG渲染速度快，仿射变换速度快。最重要的是，如果你有一个像素卷积PSF的MoG模型，PSF卷积的仿射变换星系模型本身就是MoG，因此可以非常快速地精确计算、积分和渲染。我们提出了可以直接用于图像拟合的工作示例;我们自己正在使用它们。我们提供的MoG配置文件可以在任何图像拟合代码中替换标准模型;它们将我们项目中的模型拟合速度提高了一个数量级;它们应该使任何代码更快，而基本上没有精度方面的成本。
英文摘要：
Exponential, de Vaucouleurs, and Sérsic profiles are simple and successful models for fitting two-dimensional images of galaxies. One numerical issue encountered in this kind of fitting is the pixel rendering and convolution (or correlation) of the models with the telescope point-spread function (PSF); these operations are slow, and easy to get slightly wrong at small radii. Here we exploit the realization that these models can be approximated to arbitrary accuracy with a mixture (linear superposition) of two-dimensional Gaussians (MoGs). MoGs are fast to render and fast to affine-transform. Most importantly, if you have a MoG model for the pixel-convolved PSF, the PSF-convolved, affine-transformed galaxy models are themselves MoGs and therefore very fast to compute, integrate, and render precisely. We present worked examples that can be directly used in image fitting; we are using them ourselves. The MoG profiles we provide can be swapped in to replace the standard models in any image-fitting code; they sped up model fitting in our projects by an order of magnitude; they ought to make any code faster at essentially no cost in precision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
