arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2404.04484astro-ph.GA
收藏

从SDSS图像中自动检测星系环

Automated Detection of Galactic Rings from SDSS Images

Linn Abraham, Sheelu Abraham, Ajit K. Kembhavi, N. S. Philip, A. K. Aniyan, Sudhanshu Barway, Harish Kumar

AI总结:

本研究应用深度学习技术从SDSS DR18星系图像中自动检测环状结构,生成了包含29420个星系的目录,其中4855个高置信度环星系,并发现2087个同时具有棒和环的星系,为研究星系形态结构的起源提供了重要资源。

摘要:

星系中的形态特征,如旋臂、棒、环、潮汐尾等,携带着关于其结构、起源和演化的信息。因此,对这些特征进行编目和研究,并将它们与其他基本星系性质、星系所处的环境以及与其他星系的相互作用关联起来,是非常重要的。当前和未来的星系数据量如此之大,以至于由天文学家专家通过视觉检查来识别形态特征的传统方法已不再足够。因此,有必要使用基于人工智能的技术,如机器学习和深度学习,来快速高效地寻找形态结构。在本研究中,我们报告了应用深度学习从Sloan Digital Sky Survey (SDSS) 数据发布DR18的星系图像中寻找环状结构。我们使用Buta (2017) 编制的SDSS环星系目录来训练网络,达到了良好的准确率和召回率,并生成了一个包含29420个星系的目录,其中4855个星系具有预测置信度超过90%的环状结构。利用Abraham等人 (2018) 使用深度学习技术识别的棒状星系图像目录,我们识别出2087个同时具有棒和环的星系集合。该目录对于理解这些重要形态结构的起源应该非常有用。作为该目录实用性的一个例子,我们探索了样本中环星系的环境和恒星形成特征。

英文摘要:

Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other basic galaxy properties, the environment in which the galaxies are located and their interactions with other galaxies. The volume of present and future data on galaxies is so large that traditional methods, which involve expert astronomers identifying morphological features through visual inspection, are no longer sufficient. It is therefore necessary to use AI based techniques like machine learning and deep learning for finding morphological structures quickly and efficiently. We report in this study the application of deep learning for finding ring like structures in galaxy images from the Sloan Digital Sky Survey (SDSS) data release DR18. We use a catalog by Buta (2017) of ringed galaxies from the SDSS to train the network, reaching good accuracy and recall, and generate a catalog of 29420 galaxies of which 4855 have ring like structures with prediction confidence exceeding 90 percent. Using a catalog of barred galaxy images identified by Abraham et. al. (2018) using deep learning techniques, we identify a set of 2087 galaxies with bars as well as rings. The catalog should be very useful in understanding the origin of these important morphological structures. As an example of the usefulness of the catalog, we explore the environments and star formation characteristics of ring galaxies in our sample.

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。

↑