计算机视觉表征暗物质晕
Characterising dark matter haloes with computer vision
摘要：
这项工作探讨了计算机视觉算法识别在不同结构形成模型中形成的暗物质晕的能力。我们在一套8个数值模拟中产生最大质量晕的表面质量密度图，所有这些模拟都基于相同的初始条件，但采用不同的重力模型。该套件包括一个标准的$Λ$ CDM模型，两个版本的$f(R)$ -重力，两个版本的对称重力和三个Dvali，Gabadadze和Porrati（DGP）模型。我们使用公开可用的WND-CHARM算法提取2919图像特征，无论是从原始像素强度的地图，或从各种图像变换，包括傅立叶，小波，切比雪夫和边缘变换。在丢弃最退化的模型后，我们在区分四种不同的重力模型时实现了超过60%的单图像分类成功率，同时使用简单的加权近邻距离（WND）来定义我们的分类度量。如果包括额外的信息，如晕质量的粗略估计，这个数字可以增加到70%以上。我们发现，当图像中的噪声水平增加时，分类成功率急剧下降，但这种趋势可以通过平滑噪声数据来大大降低。我们发现Zernike矩的傅立叶变换的原始图像或其小波变换是最具描述性的功能，其次是基尼系数的几个变换和Haralick和田村纹理的原始像素数据最终预处理的边缘变换。所提出的方法是通用的，不仅适用于修改后的重力模型的表征，但可以用来分类的任何一组模型，显示其各自的结构在二维形态的变化。
英文摘要：
This work explores the ability of computer vision algorithms to characterise dark matter haloes formed in different models of structure formation. We produce surface mass density maps of the most massive haloes in a suite of eight numerical simulations, all based on the same initial conditions, but implementing different models of gravity. This suite includes a standard $Λ$CDM model, two variations of $f(R)$-gravity, two variations of Symmetron gravity and three Dvali, Gabadadze and Porrati (DGP) models. We use the publicly available WND-CHARM algorithm to extract 2919 image features from either the raw pixel intensities of the maps, or from a variety of image transformations including Fourier, Wavelet, Chebyshev and Edge transformations. After discarding the most degenerate models, we achieve more than 60% single-image classification success rate in distinguishing the four different models of gravity while using a simple weighted neighbour distance (WND) to define our classification metric. This number can be increased to more than 70% if additional information, such as a rough estimate of the halo mass, is included. We find that the classification success steeply declines when the noise level in the images is increased, but that this trend can be largely reduced by smoothing the noisy data. We find Zernike moments of the Fourier transformation of either the raw image or its Wavelet transformation to be the most descriptive feature, followed by the Gini coefficient of several transformations and the Haralick and Tamura textures of the raw pixel data eventually pre-processed by an Edge transformation. The proposed methodology is general and does not only apply to the characterisation of modified gravity models, but can be used to classify any set of models which show variations in the 2D morphology of their respective structure.
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