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arXiv 2302.11482astro-ph.GA
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用机器学习探索高红移半导体的形态

Exploring the Morphologies of High Redshift Galaxies with Machine Learning

Clár-Bríd Tohill, Steven Bamford, Christopher Conselice

摘要:

星系的形态学已经被证明可以编码演化历史,并与恒星质量,恒星形成率和过去的合并事件等物理性质密切相关。虽然本地宇宙中的大多数星系都可以根据哈勃序列进行分类,但我们对高红移时观察到的不同类型的星系知之甚少。这些星系的不规则形态使得视觉分类变得困难,随着天文学的未来由许多“大数据”调查组成,我们需要一个有效的,无偏见的分类系统。在这项工作中,我们将探索使用无监督机器学习技术,从星系图像中提取特征,将高红移星系分离成不同的形态类型的机器学习集群的基础上。我们在以前的工作基础上进行了扩展,在提取特征之前解决了观测偏差,如星系的方向、表观大小和噪声,从而减少了集群的数量,并迫使网络学习有意义的特征。然后,我们比较提取的集群的物理特性,调查组之间的分离。

英文摘要:

The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local universe can be classified on the Hubble sequence, little is known about the different types of galaxies we observe at high redshift. The irregular morphology of these galaxies makes visual classifications difficult, and with the future of astronomy consisting of many "Big Data" surveys we need an efficient, and unbiased classification system in place. In this work we explore the use of unsupervised machine learning techniques to preform feature extraction from galaxy images to separate high redshift galaxies into different morphological types based on the machine learning clusters. We expand on previous work by addressing observation biases such as the orientation, apparent size of the galaxies and noise before extracting features, thus reducing the number of clusters and forcing the network to learn meaningful features. We then compare the extracted clusters' physical properties, to investigate the separation between the groups.

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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