Placeholder 3 188491942 Assigned ID: 798 ----- Attempting Submission 1 (Fri Apr 05 21:36:51 GMT 2019) ----- HST Phase I Proposal 798 (HST_ARCC_2019.apt) successfully submitted. Receipt: # 798-1 ----- Attempting Submission 2 (Fri Apr 05 22:45:52 GMT 2019) ----- HST Phase I Proposal 798 (HST_ARCC_2019.apt) successfully submitted. Receipt: # 798-2 ----- Attempting Submission 3 (Fri Apr 05 23:45:02 GMT 2019) ----- 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. Project PI and University of Minnesota postdoc Hugh Dickinson is the PI of a funded HST AR project to constrain the evolution of the Hubble Parameter using Cosmic Chronometers. He is a member of the WISP and Galaxy Zoo collaborations and has experience reducing and analyzing HST imaging and spectroscopic data. He has also developed code for the Zooniverse web platform. University of Minnesota postdoc Vihang Mehta is a member of the WISP, UVUDF and SPLASH collaborations. His experise includes HST data reduction for ACS and WFC3, galaxy evolution (particularly star-formation) and simulations of realistic galaxy images. Drs. Scarlata and Fortson are faculty in the School of Physics and Astronomy at the University of Minnesota. Dr. Scarlata is an expert in galaxy classification and evolution. Together with Dr. Fortson she is the PI of a number of projects to optimize science output for next generation surveys with joint crowdsourced and automated classification techniques. She is also an expert in the analysis of large datasets, such as those obtained for the COSMOS and WISP surveys. Dr. Fortson is Zooniverse PI at the University of Minnesota and is overseeing the integration of machine learning techniques into the Zooniverse platform. UMN Graduate student Dominic Adams is co-advised by Drs. Fortson and Scarlata. Unfunded collaborator from Oxford University, Chris Lintott is PI of Galaxy Zoo and Zooniverse. He supervises Oxford graduate student Mike Walmsley who has worked extensively on deep learning for galaxy morphology and faint galaxy features. Hespearheaded the deployment of BCNNs for active learning applied to morphological classification in Galaxy Zoo. All collaborators are science team members of Galaxy Zoo. UMN and Oxford under Fortson, Scarlata and Lintott's supervision have delivered the major Galaxy Zoo catalog papers. Dr. Simmons is an expert in both citizen science and galaxy morphology, including the intersection of human and machine classifications of galaxies and their application to studies of secular galaxy evolution. Dr. Simmons also has expertise in HST image reduction and analysis from large extragalactic surveys and is a member of the GOODS, COSMOS and CANDELS legacy survey teams. Dr. Rafelski is an expert at reducing and analyzing WFC3 and ACS data, has experience measuring the photometry and morphology of high-redshift galaxies, and is an expert at galaxy evolution studies including the analysis of clumpy galaxies. Galaxies and the IGM Cosmology 0 Regular false false false false true SUPPORTED