利用哈勃空间望远镜档案：21,926个相互作用星系的星表
Harnessing the Hubble Space Telescope Archives: A Catalogue of 21,926 Interacting Galaxies
AI总结：
利用ESA Datalabs平台和Zoobot卷积神经网络分析HST档案图像，构建了包含21,926个相互作用星系的星表，其中65%为新发现，展示了高效档案分析能力。
摘要：
并合在星系形成和演化中扮演着复杂的角色。要不断提高我们对这些系统的理解，需要越来越大的样本，而从单个巡天中选取这样的样本可能很困难（甚至不可能）。我们利用新平台ESA Datalabs，从哈勃空间望远镜科学档案中汇编了一个相互作用星系的星表；该星表比先前发表的星表大了近一个数量级。具体而言，我们将Zoobot卷积神经网络直接应用于HST $F814W$图像的整个公共档案，并对哈勃源星表中的1.26亿个源进行了概率性相互作用预测。我们结合自动化视觉表征和视觉分析，识别出一个包含21,926个相互作用星系系统的干净样本，其中大多数系统的红移$z < 1$。这些系统中，有65%在NASA河外数据库或Simbad中没有任何先前的参考文献。在去除污染的过程中，我们还发现了许多其他有趣的天体，如引力透镜、侧视原行星盘和“背光”重叠星系。我们简要研究了该样本的基本性质，并将我们的星表公开供社区使用。除了提供一份由HST成像的科学上有趣天体的新星表外，这项工作还展示了ESA Datalabs工具在促进大规模档案分析方面的能力，而无需给终端用户带来高计算或存储负担。
英文摘要：
Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems require ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalogue of interacting galaxies from the Hubble Space Telescope science archives; this catalogue is larger than previously published catalogues by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST $F814W$ images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalogue. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with $z < 1$. Sixty five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and `backlit' overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalogue publicly available for use by the community. In addition to providing a new catalogue of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.
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