Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical databases in the information era it can be assumed that very many outlier galaxies are included in existing and future astronomical databases. However, manual search for these objects is impractical due to the required labor, and therefore the ability to detect such objects largely depends on computer algorithms. This paper describes an unsupervised machine learning algorithm for automatic detection of outlier galaxy images, and its application to several Hubble Space Telescope fields. The algorithm does not require training, and therefore is not dependent on the preparation of clean training sets. The application of the algorithm to a large collection of galaxies detected a variety of outlier galaxy images. The algorithm is not perfect in the sense that not all objects detected by the algorithm are indeed considered outliers, but it reduces the dataset by two orders of magnitude to allow practical manual identification. The catalogue contains 147 objects that would be very difficult to identify without using automation.

中文翻译：

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哈勃太空望远镜星系图像异常值的自动识别

罕见的河外天体可以携带有关过去、现在和未来宇宙的大量信息。鉴于信息时代天文数据库的规模，可以假设现有和未来的天文数据库中包含非常多的异常星系。然而，由于所需的劳动，手动搜索这些对象是不切实际的，因此检测此类对象的能力在很大程度上取决于计算机算法。本文描述了一种用于自动检测离群星系图像的无监督机器学习算法，及其在哈勃太空望远镜的几个领域中的应用。该算法不需要训练，因此不依赖于干净训练集的准备。将该算法应用到大量星系中，检测到了各种异常星系图像。该算法并不完美，因为并非算法检测到的所有对象都确实被视为异常值，但它将数据集减少了两个数量级以允许实际的手动识别。该目录包含 147 个对象，如果不使用自动化将很难识别这些对象。