Constraining Dark Matter through Stellar Kinematics and Simulation-based Inference
Tri Nguyen
CIERA Postdoctoral Fellow, Northwestern University

The particle nature of dark matter (DM) is among the greatest open questions in modern astrophysics and cosmology. While the Lambda-Cold Dark Matter model successfully predicts the large-scale structure of the Universe, it faces significant challenges on galactic and sub-galactic scales, where discrepancies between observations and theoretical predictions persist. The phase-space distribution of DM in the Milky Way and nearby dwarf galaxies is highly sensitive to DM properties such as particle mass and self-interaction, providing a promising avenue for investigation. Dwarf galaxies and stellar streams, in particular, carry subtle signatures of DM physics, and thus are exceptional testbeds for probing its nature. In this talk, I will explore how stellar kinematics in these systems can be leveraged to map the DM distribution. I will also highlight the transformative role of machine learning, particularly simulation-based inference, in addressing the complexities of this problem and enabling more precise constraints on the particle properties of DM.

Date:	Jeudi, le 6 février 2025
Heure:	13:30
Lieu:	Pour tous
	Pavillon MIL A-3541