Probing Galaxy Evolution Through Time with Deep Learning

The shapes and dynamics of galaxies throughout the Universe provide a powerful lens through which we can understand the processes that shape them.

Accurately labelling the morphologies of galaxies is a vital task in the study of galaxy evolution, and is a task that until recently was best completed by humans.

Citizen science projects, such as Galaxy Zoo, have provided astronomers with hundreds of thousands of labelled galaxy images, revealing the intricate structures and histories held within.

But, in the coming era of Big Data Astronomy, machine learning and AI are becoming a popular alternative to handle the vast scale of next-generation sky surveys. This PhD project will aim to further develop advanced image recognition and object detection technologies.

Working with cutting-edge deep imaging, such as the Legacy Survey of Space and Time, and the Subaru Strategic Program on the Hyper-Supreme Cam, it will produce machine-labelled morphological catalogues of millions of galaxies, and uncover the changing nature of galaxy structure through cosmic time.