Statistical inference for strong gravitational lensing observations in the presence of dark matter

We study the properties of strong lenses generated by dark matter galaxy halos in the Universe and the statistical inference process to understand the density profile of the lens. For that, we used mock data of strong gravitational lenses using the publicly available software paltas, which is a pipeline based on the well-known package lenstronomy and written in Python. The deflectors are modeled according to the so-called elliptical power law lens profile, and the background cosmology is assumed to be that of the standard cosmological model lambda CDM. The generated lensing observations include the contribution of dark matter substructure in the form of either subhalos or halos along the line of sight. In both cases, the underlying profile is the Navarro-Frenk-White one, which corresponds to the cold dark matter model. Performing a statistical inference process on these mock observations, our final objective is to identify the influence of the substructure on the inferred parameters that describe the lens profile.