A glimpse into the ‘core-cusp’ problem in clusters of galaxies : a combination of Hubble Space Telescope imaging, VLT/MUSE spectroscopy, and strong gravitational lensing

Abstract

Clusters of galaxies play a key role in studying the distribution of dark matter (DM) throughout the universe. As the most massive observable objects in the Uni- verse, they represent the largest concentrations of DM one can study with current ground- and space-based telescopes. However, the shape of their DM density profiles in the inner region is currently a source of tension between observations and theory. While cold DM (CDM, the favored DM candidate today) cosmological simulations predict mass profiles that follow a steep power law, where the density increases in a ’cusp’ in the center, observations suggest that instead it flattens out, forming an almost constant central density ‘core’. From an observational perspective, resolving this core-cusp problem requires robust mass distribution models for many different galaxy clusters to examine the structure of their density profiles. In this thesis, I present precise mass models of eight different clusters, created using a combination of gravitational lensing and stellar kinematics thanks to imaging with the Hubble Space Telescope and spectroscopy with the Very Large Telescope. Strong lensing is one of the most powerful tools for obtaining clusters’ total mass profiles, as it is purely geometric, independent of clusters’ dynamical states, and relying princi- pally on the identification of multiply lensed background galaxies to constrain the model. The inner DM profile can then be recovered from the total mass profile by subtracting the baryonic component, which I model using stellar kinematics of the brightest cluster galaxy (BCG) as a proxy for the total stellar mass in the cluster center. The slope of the cluster sample, on average, is core-like. I ultimately aim to statistically examine what physical processes might account for these core-like slopes to distinguish between potential DM candidates, such as self-interacting DM, warm DM, or even more exotic types of DM.