论文标题
特征加仑：使用图像空间中的主要成分来描述星系形态
Eigengalaxies: describing galaxy morphology using principal components in image space
论文作者
论文摘要
我们证明了如何通过“特征加拉克西”的加权总和来表示星系形态，以及如何在概率框架中使用特征，以在各种应用中启用原则和简化的方法。特征加仑可以源自单频或多波段图像集的主成分分析（PCA）。它们编码了可以组合的基础向量的图像空间等效物，以大规模降低的方式描述大型星系的结构特性。作为例证，我们展示了哈勃太空望远镜烛台中的10,243个星系的样本如何仅由12个特征加拉克斯表示。我们详细说明了如何衍生和测试此图像空间。我们还描述了PCA（PPCA）的概率扩展，该扩展可以使特征加拉克斯框架为星系分配概率。我们提出了与下一代大型成像调查特别相关的概率特征性框架的四个实际应用：我们（i）可能会表明，假期星系对自然候选者进行自然候选者的距离（ii）表明如何在exemplars上表现出了如何在exemplors上表现出如何表现出的（III）的遗漏（ii）表明如何在对象上表现出（iiv）的方式（iiv）。
We demonstrate how galaxy morphologies can be represented by weighted sums of "eigengalaxies" and how eigengalaxies can be used in a probabilistic framework to enable principled and simplified approaches in a variety of applications. Eigengalaxies can be derived from a Principal Component Analysis (PCA) of sets of single- or multi-band images. They encode the image space equivalent of basis vectors that can be combined to describe the structural properties of large samples of galaxies in a massively reduced manner. As an illustration, we show how a sample of 10,243 galaxies in the Hubble Space Telescope CANDELS survey can be represented by just 12 eigengalaxies. We show in some detail how this image space may be derived and tested. We also describe a probabilistic extension to PCA (PPCA) which enables the eigengalaxy framework to assign probabilities to galaxies. We present four practical applications of the probabilistic eigengalaxy framework that are particularly relevant for the next generation of large imaging surveys: we (i) show how low likelihood galaxies make for natural candidates for outlier detection (ii) demonstrate how missing data can be predicted (iii) show how a similarity search can be performed on exemplars (iv) demonstrate how unsupervised clustering of objects can be implemented.
