论文标题
识别具有卷积神经网络的AGN宿主星系
Identifying AGN host galaxies with convolutional neural networks
论文作者
论文摘要
活跃的银河核（AGN）是超级质量黑洞，具有在某些星系中发现的发光积聚磁盘，被认为在星系进化中起着重要作用。但是，用于识别AGN的传统光谱需要时间密集型观察。我们使用210,000个Sloan数字天空调查星系样本来训练卷积神经网络（CNN），以区分AGN宿主星系和非活动星系。我们评估了33,000个星系中的CNN，这些星系被分类为复合材料，并发现星系外观及其CNN分类之间的相关性，这暗示了影响星系形态和AGN活性的进化过程。随着Vera C. Rubin天文台的出现，Nancy Grace Roman Space望远镜和其他宽场成像望远镜，深度学习方法将有助于快速，可靠地列出的AGN样本，以供未来分析。
Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. However, traditional optical spectroscopy for identifying AGN requires time-intensive observations. We train a convolutional neural network (CNN) to distinguish AGN host galaxies from non-active galaxies using a sample of 210,000 Sloan Digital Sky Survey galaxies. We evaluate the CNN on 33,000 galaxies that are spectrally classified as composites, and find correlations between galaxy appearances and their CNN classifications, which hint at evolutionary processes that affect both galaxy morphology and AGN activity. With the advent of the Vera C. Rubin Observatory, Nancy Grace Roman Space Telescope, and other wide-field imaging telescopes, deep learning methods will be instrumental for quickly and reliably shortlisting AGN samples for future analyses.
