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Project TitleGalaxy Evolution Computational Astrophysics
SummaryHow did the Milky Way's disk system form? Today, we lack a deep understanding of the genesis and evolution of this important stellar structure. This project will explore how future astronomical surveys could yield deep insight in the origin of the Milky Way's disk system using spatial, chemical and dynamic data. The intern will analyze snapshots from new, state-of-the-art simulations of Milky-Way-like galaxies by generating mock data corresponding to next-generation astronomical surveys.
Job DescriptionThe intern will investigate the types of future observations needed to fully characterize the Milky Way's disk system, based on state-of-the-art, ultra-high-resolution simulations generated using the Feedback In Realistic Environments (FIRE) code. The intern will use a new code currently in production to generate synthetic stellar catalogs from the simulation snapshots, incorporate realistic observational errors into the catalogs, and investigate which types or combinations of future surveys (photometric, radial-velocity, or proper-motion) can reliably identify stars that constrain the Milky Way disk's formation history. As part of this process the intern will test parallelization schemes for the code being developed to create the stellar catalogs, which is not yet parallelized.
Use of Blue WatersThe generation and analysis of synthetic stellar catalogs from the simulation snapshots will require the unique combination of parallel processing and large local memory access that Blue Waters can provide. A single snapshot from one of the high-resolution simulations to be used for this project is between 10 and 15 GB in size, comparable to the memory accessible to XK7 nodes on Blue Waters. The synthetic catalogs will be of comparable or larger size, so both generating (in batch mode) and analyzing (in interactive mode) the mock data will require significant memory and fast data access. Sampling synthetic stars from the snapshot can and should be straightforwardly parallelized given that the number of stars to be generated increases extremely quickly as one approaches the depths that future surveys will achieve. For example, a synthetic survey of comparable depth to a single pass of the Large Synoptic Survey Telescope would consist of several billion synthetic stars, and multiple catalogs from each of several simulations are necessary to understand the stochastic variation between different simulated galaxies and different viewpoints within a galaxy. The project will therefore also make use of Blue Waters resources to test parallelization schemes for rapid catalog generation.
Conditions/QualificationsMust have python programming experience.
Start Date05/31/2018
End Date05/31/2019
LocationDepartment of Physics
University of California
Davis, California
Preet Patel