所有SDSS/DR7星系光谱的自动无监督分类
Automatic unsupervised classification of all SDSS/DR7 galaxy spectra
摘要：
使用“k-means”聚类分析算法，我们进行了第七次和最后一次斯隆数字巡天数据发布（SDSS/DR 7）的所有星系光谱的无监督分类。除了向restframe波长的偏移和向g带通量的归一化之外，没有对原始光谱进行任何操作。该算法保证具有相似光谱的星系属于同一类。我们发现，99%的星系可以被分配到只有17个主要类别，与11个额外的次要类别，包括其余的1%。分类是不是唯一的，因为许多星系出现在类之间，然而，我们的渲染算法克服了这个弱点，用一个工具来识别边缘星系。每一个类别都有一个模板光谱，这是该类别中所有星系光谱的平均值。这些低噪声模板光谱沿着标记为从0到27的序列平滑且连续地变化，从最红的类到最蓝的类。我们的基于自动光谱K均值（ASK）的分类将星系按颜色分开，具有红色序列，蓝色云和绿色山谷的特征。当红序星系和绿谷星系出现发射线时，它们是活动星系核活动的特征。蓝色星系的发射线对应于恒星形成区。我们发现光谱类和哈勃类型之间的预期相关性，但这种关系表现出较高的内在散射。ASK分类的几个潜在的用途被确定和勾画，包括快速确定的物理性质的插值，类作为模板的红移测定，和目标选择在后续工作（我们发现类的塞弗特星系，绿谷星系，以及大量的离群值）。ASK分类可通过各种网站公开查阅。
英文摘要：
Using the 'k-means' cluster analysis algorithm, we carry out an unsupervised classification of all galaxy spectra in the seventh and final Sloan Digital Sky Survey data release (SDSS/DR7). Except for the shift to restframe wavelengths, and the normalization to the g-band flux, no manipulation is applied to the original spectra. The algorithm guarantees that galaxies with similar spectra belong to the same class. We find that 99 % of the galaxies can be assigned to only 17 major classes, with 11 additional minor classes including the remaining 1%. The classification is not unique since many galaxies appear in between classes, however, our rendering of the algorithm overcomes this weakness with a tool to identify borderline galaxies. Each class is characterized by a template spectrum, which is the average of all the spectra of the galaxies in the class. These low noise template spectra vary smoothly and continuously along a sequence labeled from 0 to 27, from the reddest class to the bluest class. Our Automatic Spectroscopic K-means-based (ASK) classification separates galaxies in colors, with classes characteristic of the red sequence, the blue cloud, as well as the green valley. When red sequence galaxies and green valley galaxies present emission lines, they are characteristic of AGN activity. Blue galaxy classes have emission lines corresponding to star formation regions. We find the expected correlation between spectroscopic class and Hubble type, but this relationship exhibits a high intrinsic scatter. Several potential uses of the ASK classification are identified and sketched, including fast determination of physical properties by interpolation, classes as templates in redshift determinations, and target selection in follow-up works (we find classes of Seyfert galaxies, green valley galaxies, as well as a significant number of outliers). The ASK classification is publicly accessible through various websites.
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