Probing the Nature of Dark Matter through Small-scale Substructure

Xiaolong Du (UCLA)

October 03, 2023

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Abstract: Roughly 85% of the matter in the Universe is in the form of dark matter (DM). Under gravitational interactions, DM clusters and forms hierarchical structure. Different DM models predict distinct features on small scales. For example, the standard cold dark matter (CDM) model predicts the formation of small halos down to earth mass scale, which may not exist in other DM models such as the warm dark matter. Thus to detect these small halos (<10^8 Msun) is the key to distinguish different DM candidates. Observations based on gravitational interactions, such as the strong gravitational lensing, has helped to detect the smallest DM halos so far even if they do not contain detectable stars. With more upcoming observational data, we expect to push the lowest detection limit to even small scales. At the same time, it becomes crucial to have a fast but accurate way to predict the properties of these small (sub)halos for a variety of DM models. In this talk, I will show that using sim-analytic models we are able to produce robust predictions for DM substructure, such as the mass function, spatial distribution and internal density profiles. Our models will help to better interpret the observation data and put a more robust constraint on DM models.