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arXiv 1002.0676astro-ph.CO
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星系形态的软聚类分析:SDSS的一个工作实例

Soft clustering analysis of galaxy morphologies: A worked example with SDSS

Rene Andrae, Peter Melchior, Matthias Bartelmann

摘要:

内容:在现代巡天中,星系数量巨大且仍在快速增长,这就需要一种自动化和客观的分类方法。无监督学习算法特别令人感兴趣,因为它们自动发现类。目的:我们简要讨论了过于简化的分类方法的陷阱,并概述了一种替代方法,称为“聚类分析”。方法:根据不同的分类方法的能力进行分类。基于这种分类,我们提出了一种概率分类算法,自动检测数据首选的最佳类别。我们探讨了该算法的可靠性,在系统的测试。使用一个小样本的明亮星系的SDSS,我们证明了这种算法在实践中的性能。在这种情况下,我们能够解开星系形态的分类和参数化的问题。结果:我们给物理参数的概率分类方案是必要的。我们提出的算法产生合理的形态类和对象到类的分配没有任何事先的假设。结论:有一些复杂的自动分类算法可以满足所有必要的要求,但在解释结果方面仍然需要做大量的工作。

英文摘要:

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover classes automatically. Aims: We briefly discuss the pitfalls of oversimplified classification methods and outline an alternative approach called "clustering analysis". Methods: We categorise different classification methods according to their capabilities. Based on this categorisation, we present a probabilistic classification algorithm that automatically detects the optimal classes preferred by the data. We explore the reliability of this algorithm in systematic tests. Using a small sample of bright galaxies from the SDSS, we demonstrate the performance of this algorithm in practice. We are able to disentangle the problems of classification and parametrisation of galaxy morphologies in this case. Results: We give physical arguments that a probabilistic classification scheme is necessary. The algorithm we present produces reasonable morphological classes and object-to-class assignments without any prior assumptions. Conclusions: There are sophisticated automated classification algorithms that meet all necessary requirements, but a lot of work is still needed on the interpretation of the results.

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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