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arXiv 1010.3018astro-ph.COastro-ph.IM
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在SDSS DR7光谱样品中重新审视哈勃序列:一个公开可用的哈勃自动分类

Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classification

Marc Huertas-Company, J. A. L Aguerri, M. Bernardi, S. Mei, J. Sánchez Almeida

摘要:

我们提出了一个自动形态分类的4种类型(E,S 0,Sab,Scd)的~700.000星系SDSS DR 7光谱样本的基础上支持向量机。分类的主要新属性是,我们将每个星系关联到四个形态类中的概率,而不是分配一个单一的类。因此,分类更好地适应自然,我们期望不同形态类型之间的连续过渡。该算法使用视觉分类进行训练,然后与几个独立的视觉分类进行比较,包括Galaxy Zoo首次发布的目录。我们发现自动分类和经典的视觉之间有很好的相关性。编译的目录旨在用于不同的应用程序,可以在http://gepicom04.obspm.fr/sdss_morphology/Morphology_2010.html下载,很快就可以从CasJobs数据库下载。

英文摘要:

We present an automated morphological classification in 4 types (E,S0,Sab,Scd) of ~700.000 galaxies from the SDSS DR7 spectroscopic sample based on support vector machines. The main new property of the classification is that we associate to each galaxy a probability of being in the four morphological classes instead of assigning a single class. The classification is therefore better adapted to nature where we expect a continuos transition between different morphological types. The algorithm is trained with a visual classification and then compared to several independent visual classifications including the Galaxy Zoo first release catalog. We find a very good correlation between the automated classification and classical visual ones. The compiled catalog is intended for use in different applications and can be downloaded at http://gepicom04.obspm.fr/sdss_morphology/Morphology_2010.html and soon from the CasJobs database.

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。

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