Strong gravitational lensing as a cosmological probe: new ways of constraining dark

Abstract

Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter, and galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark matter on small scales at cosmological distances from the Milky Way. 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. We present recent advances in both of these approaches. With respect to the former, we present advances in the analysis of gravitational lenses and identification of small-scale clumps using machine learning. With respect to the latter, we introduce the convergence power spectrum as a promising statistical observable that can be extracted from strong lens images and used to distinguish between different dark matter scenarios, showing how different properties of the dark matter get imprinted on different scales. We include a discussion on the different contribution of substructure and line-of-sight structure to perturbations in strong lens images.