Federal Grant Award Summary

The National Science Foundation's Division of Astronomical Sciences awarded $445,607 to the University of Virginia under the Mathematical and Physical Sciences (CFDA 47.049) program for a five-year CAREER grant spanning August 1, 2026 through July 31, 2031. The project will develop an artificial intelligence (AI)-based framework for quantifying uncertainties in cosmological simulations of galaxy formation. Primary deliverables include the creation of a fully automated simulation pipeline capable of executing thousands of zoom-in simulations at varying resolution and halo mass scales, with outputs sampled across multiple physical parameters including supernova feedback strength, Axially Grommet Number (AGN) efficiency, dark matter density, and matter clustering amplitude. The research team will employ graph neural networks (GNNs) paired with diffusion-based generative models to predict galaxy properties across sampled parameter spaces and enable direct comparison across physical models, thereby providing a statistically grounded framework for testing small-scale tensions in Lambda Cold Dark Matter (Lambda-CDM) cosmology.

Educational and workforce development components include support for a graduate research student participating in model development, a Research Experiences for Undergraduates program, and an annual schedule of teacher training activities. Research will be conducted at the University of Virginia's Charlottesville campus. The project advances galaxy formation theory by establishing systematic uncertainty quantification methodologies applicable across cosmological assumptions and resolution scales, directly supporting the NSF's mission to strengthen the nation's scientific enterprise through fundamental research and education.