One of the main goals for modern observational cosmology is to discover and understand how galaxies and their constituent substructures have assembled and evolved throughout cosmic history. The diverse observed morphologies of individual galaxies are not only indicative of their current composition, but also encode a detailed record of their assembly histories, their past and ongoing star formation, and their interaction with local environments. Studying large populations of galaxies allows coarse morphological characteristics and intrinsic physical processes to be statistically connected. While the simplest techniques for automatic morphological classification have largely solved the problem of coarse morphological classification for large populations of galaxies, they cannot identify more subtle features like stellar shells, spiral arms and bars. We propose using cloud computing infrastructure to develop a publically available toolkit to identify and measure intricate substructure in galaxies. The toolkit will use Deep Machine Learning to perform clump detection, with training labels derived from crowdsourcing. We will use our new toolkit in conjunction with the cloud-hosted MAST data archive on AWS to perform a comprehensive re-analysis of the Hubble Space Telescope (HST) imaging data archive to identify, localize and classify giant star-forming clumps within detected galaxies at all redshifts. We will use this sample to study how the prevalence and properties of star forming clumps evolve with cosmic time and compare our findings with theoretical predictions. ...
|Publication status||Published - Jun 2019|