Dark matter cores: primordial versus expanded

Supervisors: Glenn van de Ven, Tanja Rindler-Daller, and Ryan Leaman (Department of Astrophysics), in collaboration with Sabine Thater and Gerhard Hensler (Department of Astrophysics)

Funding Situation: potentially via a VISESS PhD fellowship

Project outline: The nature of dark matter (DM) is one of the major unsolved problems in astronomy and physics. The cold dark matter (CDM) paradigm predicts concentrated DM density profiles with an inner cusp, while measurements based on gas and stellar kinematics in dwarf galaxies reveal shallower DM density profiles with an inner core: the cusp-core problem. The proposed solution is that due to the sudden removal of gas by stellar feedback, the DM expands into a cored density. In alternative non-CDM models with self-interaction or scalar-field dark matter (SIDM, SFDM), the formation of a central density peak is avoided from the start.

To differentiate whether cores are the result of CDM+feedback or non-CDM alternatives, we propose to investigate the full 3D DM density distribution, i.e., radial profile and shape: whereas stellar feedback can turn DM cusps into cores while preserving the flattened geometries in CDM, the particle interactions in SIDM create round cores, and characteristic ellipsoidal inner shapes are predicted in SFDM models.

Our approach is a synergy between theory and observations: We will build on our current theoretical models for SFDM and SIDM to run a large suite of accurate single-halo formation simulations including baryons; this will inform us how the DM particle nature affects the 3D DM density, including the 'primordial' core size. Observationally, we will fit our population-dynamical models to existing observed motions of different stellar populations, of gas and of globular clusters in nearby DM-dominated dwarf galaxies to infer the 3D DM density, including the 'observed' core size. By comparing the inferred radial profile and shape pairs with our theoretical predictions, we aim to significantly constrain and possibly rule out non-CDM models.