Morphological classification of galaxies by shapelet decomposition in the Sloan Digital Sky Survey
Morphological classification of galaxies by shapelet decomposition in the Sloan Digital Sky Survey
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DOI：
10.1086/380934
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发表时间：
2004-02-01
影响因子：
5.3
通讯作者：
McKay, TA
中科院分区：
文献类型：
--
作者：
Kelly, BC;McKay, TA
我们描述的应用程序的“shapelet”的线性分解星系图像形态分类使用的图像类似于3000星系从斯隆数字巡天。在分解星系后，我们进行主成分分析，以减少shapelet空间的维数为9。我们发现，这九个主成分中的每一个包含独特的形态信息，并给出了每个主成分的星系的形态的贡献的描述。我们发现，不同的哈勃类型的星系分离干净的shapelet空间。我们将高斯混合模型应用到由主成分构成的九维空间，并将结果作为分类的基础。利用混合模型，我们将星系分为七类，并对每类星系的物理和形态特征进行了描述。我们发现，几个混合模型类与传统的哈勃类型在形态和物理参数（例如，例如，在一个实施例中，颜色、速度分散等）。此外，我们还发现了另一类晚型形态，但具有高速色散和非常蓝的颜色，这些星系中的大多数表现出poststarburst活动。该方法提供了一个客观和定量的替代传统和主观的视觉分类。
We describe application of the "shapelet'' linear decomposition of galaxy images to morphological classification using images of similar to 3000 galaxies from the Sloan Digital Sky Survey. After decomposing the galaxies, we perform a principal component analysis to reduce the number of dimensions of the shapelet space to nine. We find that each of these nine principal components contains unique morphological information and give a description of each principal component's contribution to a galaxy's morphology. We find that galaxies of differing Hubble type separate cleanly in the shapelet space. We apply a Gaussian mixture model to the nine-dimensional space spanned by the principal components and use the results as a basis for classification. Using the mixture model, we separate galaxies into seven classes and give a description of each class' physical and morphological properties. We find that several of the mixture model classes correlate well with the traditional Hubble types both in their morphology and their physical parameters ( e. g., color, velocity dispersions, etc.). In addition, we find an additional class of late-type morphology but with high velocity dispersions and very blue color; most of these galaxies exhibit poststarburst activity. This method provides an objective and quantitative alternative to traditional and subjective visual classification.