Large scale characterisation of galaxy morphology: a deep learning approach

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The aim of this interdisciplinary project is to design data science methods and algorithms for the large-scale automatic characterisation of galaxy morphology from space telescope images. Characterising the morphology of galaxies is essential for astrophysics research, as they provide insights into the physical processes that drove their evolutionary history. However, the astrophysics community currently faces a deluge of images that can only be resolved through the development of automatic and adapted image analysis methods. This project will address this burning issue by developing the tools and methods necessary to fully exploit observational data. As such, it will greatly impact the ability of the astrophysics community to carry out high quality research based on big observational data.During this project, we will work with astrophysics collaborators to design state-of-the-art computer vision and machine learning techniques for automatically and precisely characterising the morphology of galaxies from a large range of observational data. We will investigate challenges that have never been tackled before, such as understanding, modelling, and accounting for how the appearance of galaxies vary due to redshift, or exploiting multi-spectral images simultaneously for greater robustness. These are in fact key challenges in astrophysics image analysis in general.We will also tackle the still unsolved problem of jointly detecting attributes of objects (e.g. presence of spiral arms or a bar) and estimating their intensity (e.g. number and angle of arms and thickness of a bar). Our work will be based on deep learning, a new area of machine learning that achieves state-of-the-art results for many big data and image analysis tasks and arouses widespread enthusiasm among the computer vision community. We will develop methods that further advance this new and exciting field of research, while being specifically adapted to the analysis of astrophysical images.This project is highly collaborative and multidisciplinary. It involves institutions with complementary expertise in data science, computer vision, machine learning, and deep learning (Computer Science Department at Swansea University), and in galaxy morphology and astrophysics imaging (Strasbourg Observatory (France), University of Bristol). The development of new computer vision and deep learning methods will be supported by real and simulated images provided by the astrophysics laboratories. Real image data will be obtained from several observation missions, such as the European Space Agency's Euclid mission of which the astrophysics collaborators are taking part.