Automatic Object Detection and Categorisation in Deep Astronomical Imaging Surveys Using Unsupervised Machine Learning

I present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy the technique uses no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. I demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS0416.1-2403), I show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an ‘early’ or ‘late’ type galaxy is. I present the results of testing the technique for generalisation and to identify its optimal configuration. I then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of 60000 labelled galaxies, grouped by their similarity. I show how the automatically identified groups contain galaxies with similar morphological (and photometric) type. I compare the catalogue to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping, I demonstrate a good level of concordance between them. I publicly release the catalogue and a corresponding visual catalogue and galaxy similarity search facility at www.galaxyml. uk. I show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging. Finally, I consider how the technique can be improved and applied to future surveys to identify transient objects. Declaration I declare that no part of this work is being submitted concurrently for another award of the University or any other awarding body or institution. This thesis contains a substantial body of work that has not previously been submitted successfully for an award of the University or any other awarding body or institution. The following parts of this submission have been published previously and/or undertaken as part of a previous degree or research programme: 1. Chapter 3: Sections 3.3. 2, 3.3.3 and 3.4. 2.1 were previously published in Hocking et al., 2018 Monthly Notices of the Royal Astronomical Society, 473, 1108 2. Chapter 4: Section 4.5 was previously published in Hocking et al., Unsupervised Image Analysis and Galaxy Categorisation in Multi-Wavelength Hubble Space Telescope Images Proceedings of the European Conference on Machine Learning (ECML) Doctoral Consortium 2015, 3. Chapter 5: The content of this chapter was previously published in Hocking et al., Mining Hubble Space Telescope Images, Proceedings of the International Joint Conference on Neural Networks 2017 (IJCNN) 4. Chapter 6: This content of this chapter was previously published in Hocking et al., 2018 Monthly Notices of the Royal Astronomical Society, 473, 1108 Except where indicated otherwise in the submission, the submission is my own work and has not previously been submitted successfully for any award.