Constraining the nature of dark matter via new dynamical modeling methods
Supervisor: Dr. Ryan Leaman
Contact information: ryan.leaman@univie.ac.at
Co-supervisors: Dr. Sabine Thater
Expected duration: 9 months
Project description & Goals:
Dark matter (DM) is the most important contributor to the mass budget in the Universe and instrumental in the formation of large galactic structures - including our Milky Way. However we still do not have constraints from expensive collider experiments (e.g., LHC) on what properties this fundamental particle has (e.g., mass, interaction strength). Astrophysical studies of galaxies offer a unique and complementary way to answer this question. The largest challenge in this field is then to disentangle effects due to the DM particle, from the complex baryonic processes which also alter galaxies and their DM halos (such as star formation and energetic feedback from exploding stars). Our group has developed a new way to break these degeneracies by leveraging gas and stellar kinematic data of galaxies jointly in a single dynamical model (Leung et al. 2020). By combining these two kinematic tracers of the galaxy potential (stellar velocities and kinematics of neutral or molecular hydrogen gas) in one dynamical model, we can recover key parameters of the DM distribution (DM halo flattening, inner density profile slope) which constrain DM particle properties (interaction cross-section, particle mass). To prepare for upcoming VLT-MUSE data of galaxies we are excited to work with a student who wants to develop and apply this method to simulations, as well as existing spectroscopic data of nearby galaxies. Together this will let us assess the robustness of this novel method, apply it to extant observations and investigate what type of data fidelity, galaxy properties and numbers of observations lead to unique constraints on these important cosmological parameters describing our Universe.
Working plan & Milestones (including final thesis):
- Gain familiarity with existing data/literature/dynamical models in our group
- Extract observables for the dynamical models from state of the art simulated galaxies taken out of zoom-in cosmological hydrodynamical simulations (DM + baryons).
- Use the gas kinematics together with variations in the number of stellar kinematic tracers, population sub-groups, tracer density parameterizations to assess optimal constraints on DM particle properties using the dynamical modeling tools.
- Prepare for future observations by assessing how the recovered DM halo parameters from the dynamical models (inner slope, flattening) rule out/in alternative DM cosmologies (self-interacting DM, scalar-field DM) in different sample sizes.
- Explore these optimal dynamical models on real galaxies which have variations in DM fraction, star formation history and environment.
- Write up thesis
Requirements / special skills: Interest in dynamical modeling, bridging simulations and observations. Some python (or equivalent IDL, C, R) coding and statistics would be helpful.
References:
Leung et al., 2020 ui.adsabs.harvard.edu/abs/2021MNRAS.500..410L/abstract