The formation and evolution of massive early-type galaxies is one of the open problems in cosmology: what caused them to stop forming stars? What drives their rapid size evolution? How do mergers modify their inner structure? The key for answering these questions might lie in the dark matter distribution: the dark matter halo plays a fundamental role in the evolution of galaxies, determining, for instance, the gas accretion rate. Although dark matter is notoriously difficult to measure, gravitational lensing is one of the few tools that allows us to accurately map its distribution. Strong lensing, probing scales of a few kpc, can be used to measure the inner density profile of a dark matter halo, which is sensitive to baryonic physics processes. Weak lensing is mostly sensitive to scales of hundreds of kpc, allowing us to measure total halo masses. In order to take full advantage of gravitational lensing measurements, it is often necessary to statistically combine measurements over a large number of systems. For this purpose, we developed a novel statistical method based on a Bayesian hierarchical approach.

In this talk, I will introduce the basic idea of Bayesian hierarchical inference applied to gravitational lensing, show results from recent applications of this method to data from the Hyper Suprime-Cam survey and discuss future prospects in galaxy-galaxy lensing studies. 

BIO
I obtained my PhD at the University of California, Santa Barbara, working on gravitational lensing under the supervision of Prof. Tommaso Treu. After spending 3 years as a postdoctoral fellow at the Kavli Institute for the Physics and the Mathematics of the Universe, in Japan, I am now a Marie Curie fellow at Leiden Observatory since September 2018.

Host: Prof. Dandan Xu