Automatic identification of outliers in Hubble Space Telescope galaxy images

Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical data bases in the information era, it can be assumed that very many outlier galaxies are included in existing and future astronomical data bases. However, manual search for these objects is impractical due to the required labour, 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 data set 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.

Automated summary

Rare extragalactic objects often require computer algorithms for detection due to the impracticality of manual search. This paper introduces an unsupervised machine learning algorithm for automatically detecting outlier galaxy images, reducing the data set significantly while allowing practical manual identification. The algorithm successfully identified 147 objects that would be challenging to identify without automation.