Speaker
Description
Galaxy-galaxy strong gravitational lenses provide a unique laboratory for probing small-scale structures and testing the $\Lambda$CDM paradigm. However, performing precise statistical analysis of such observations is extremely challenging since it requires disentangling the source galaxy’s light from the lens’ mass distribution and marginalizing over different dark matter substructure configurations. Research in this field can be broadly separated into works that aim to directly detect individual perturbers and works that aim to statistically constrain the matter distribution by looking at collective perturbations caused by an unresolved population of perturbers. In this talk, I will present recent advances in both of these approaches using a multi-stage method that combines parametric lensing models and an implicit-likelihood neural simulation-based inference technique. I will explain how this technique enables measuring both the properties of individual subhalos and directly the parameters of the subhalo mass function, overcoming some computational limitations of likelihood-based analyses.