Combining advanced numerical simulations and strong gravitational lensing observations to put new constraints on the nature of dark matter

The nature of dark matter is one of the most fundamental and striking open questions in modern astrophysics.

Despite the success of the favoured Cold Dark Matter (CDM) model in explaining observations of structures on scales larger than 1 Mpc, in reality a wide range of dark matter models are allowed, motivated to varying degrees by particle physics assumptions, and the tension between the CDM predictions and observations on galactic and sub-galactic scales.

A combination of simulated predictions and observational data is essential to enhance our understanding of our Universe and set constraints on dark matter from astrophysical phenomena.

The primary goal of this MSC project is to generate transformative predictions by filling the existing gap between advanced hydrodynamical simulations in the standard cold dark matter model and the alternative dark matter field, where predictions for observations have been so far often derived from dark-matter-only simulations or simulations of very small samples.

This will be achieved through (1) the creation of an innovative set of hydrodynamical simulations spanning a wide range of dark matter models and including the physics of baryons and (2) the integration with an efficient pipeline to produce realistic mock observations.

These will allow me to (3) compare the properties of simulated and observed objects and derive the new accurate predictions of the combined effect of dark matter and baryons on a number of observables, focusing on the gravitational lensing signal. I will address a number of open questions, such as: is dark matter cold, warm or self-interacting?

Will future lensing observations be able to exclude models alternative to CDM? Do these models reproduce the observed scaling relations for galaxies? What will ALMA, ELT, LSST or Euclid tell us about the nature of dark matter?

The University of Bologna is the optimum location for this project, thanks to an ideal combination of expertise and computing resources.