Eyes on the future: optimizing science output for next generation surveys with joint crowdsourced and automated classification techniques

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Future galaxy surveys will produce so much data that astronomers will no longer be able to rely on their previous methods of classifying them in order to extract the science of galaxy formation and evolution through cosmic history. Although "crowd-sourced" galaxy classifications tap into a vast resource of volunteer labor, even major efforts like the Galaxy Zoo (GZ) will not be able to keep up. This project will build on the GZ database, extending methods to other epochs in the Universe, and simultaneously use the results of the citizen science work together with machine learning to develop new automated classification tools. Using the proven research value of involving the public and the broader community, and with a new generation of intelligent computer methods, this study will build on the best of both.While crowd-sourced galaxy classifications have proven their worth on a decade of data from the Sloan Digital Sky Survey (SDSS), there remain two major challenges to making them a standard component of the data processing pipelines for the next generation of surveys. The first is proving the utility of the method at high redshifts, where more galaxies have irregular or clumpy morphologies. The second acknowledges that even crowdsourcing does not have the capacity for the data volume and rates that are to come, requiring new more sophisticated machine classification algorithms. This project will develop catalogs for high-redshift crowd-sourced data and for simulated galaxies, and develop a new automated classification tool that extends to higher redshifts with a training pipeline adaptable to multiple galaxy surveys. It includes three science projects: (a) directly constrain galaxy size and mass growth rates; (b) measure any relationship between bar-dominated disks and fueling of active galactic nuclei, and (c) quantify the demographics and evolution of disk sub-structures. Efforts to automate morphological classifications using parametric and non-parametric techniques have been reasonably successful for SDSS, but did not extend to deriving the necessary detailed structural parameters. The GZ crowd-sourcing project has been successful beyond expectations, providing scientifically viable parameters from volunteer work and leading to over a hundred peer-reviewed papers. This study will extend both of these approaches in preparation for much larger future surveys, which expect to produce as much data per night as ten years of SDSS.The high-level catalogs to be produced, and the new classification algorithms to be used, are to be released publically, and will be a valuable resource for the community. Along with informal guided-inquiry projects, GZ will be implemented into undergraduate astronomy courses. Students involved in this work will get well-defined PhD projects and acquire technical skills valuable in their future professional careers.