Collaborative Research: CDS&E: Systematic Predictions for Dynamical Signatures of New Dark Matter Physics in Galaxies

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Dark matter is a mysterious substance that does not emit, absorb, or reflect light, yet makes up over 80% of the matter in our Universe. Its existence is inferred through the gravitational force it exerts on visible matter, but its identity remains one of the driving scientific questions of our time. Since scientists have not directly detected dark matter particles, the goal of much current research, including this proposal, is to predict ways to indirectly constrain dark matter’s properties. The team of scientists at the University of Pennsylvania, MIT, and Princeton, will study how to test dark matter with individual galaxies. The team will implement several well-motivated models for dark matter in simulations of galaxies like the Milky Way and smaller, creating for the first time a set of controlled experiments in galaxy formation where only the type of dark matter is varied. They will use these simulations to identify which indirect tests can use observations of galaxies to distinguish between dark matter models and make predictions for those tests tailored to next-generation observatories. The team will reach across several traditionally siloed subfields of physics to give a new generation of diverse researchers the broad theoretical and computational background needed for this groundbreaking work. By implementing evidence-based best practices to foster equity within their collaboration, this team will make a significant advance toward growing a more inclusive computational astrophysics community. Specifically, the main outcomes of the proposed work are: (1) a new, public set of validated software modules implementing key classes of DM particle models in the well-developed, extensively tested GIZMO codebase for cosmological-hydrodynamical simulations of galaxy formation; (2) a public set of simulated Milky Way-like and dwarf galaxies with identical initial conditions, and exactly the same baryonic physics, evolved under a variety of DM models; (3) a set of concrete, observationally testable predictions—derived from traditional and machine-learning-based analyses—for current and future observatories that can be used to constrain or rule out classes of DM models; (4) a network of new graduate researchers and postdocs with the broad training and expertise to complete, for the first time, the connection between theoretical models of dark matter, the study of galaxy formation and observational predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.