Thesis

Galaxy clusters, which form at the intersection of matter filaments, are excellent tracers of the large-scale matter distribution in the Universe and are a valuable source of information for cosmology.
The sensitivity of the Euclid space mission (launch in 2023) allow blind detection of galaxy clusters through gravitational lensing (i.e. directly linked to the projected total mass). Combined with its wide survey area (14,000 deg²), Euclid should allow the construction of a galaxy cluster catalogue that is unique in both its size and selection properties.
In contrast to existing cluster catalogues, which are typically based on baryonic content (e.g., X-ray emission from intra-cluster gas, the Sunyaev-Zel’dovich effect in the millimeter regime, or optical emission from galaxies), a catalogue derived from gravitational lensing is directly sensitive to the total mass of the clusters. This makes it truly representative of the underlying cluster population, a significant advantage for both galaxy cluster studies and cosmology.
Within the Euclid mission, S. Pires coordinates the activities related to galaxy clusters selected through weak gravitational lensing (WL), including the production and scientific exploitation of the corresponding cluster catalogue. In this context, we have developed a new multi-scale detection method to identify galaxy clusters using only the weak-lensing signal. Developed by our team, this method has been pre-selected for the construction of the Euclid’s weak lensing galaxy cluster catalogue.
The goal of this PhD project is to build and characterize the galaxy cluster catalogue identified via weak lensing in the data collected during the first year of Euclid observations (DR1), based on this detection method. The candidate will derive cosmological constraints from the modelling of the cluster abundance, using the classical Bayesian framework. In parallel, we will develop a complementary simulation-based inference (SBI) analysis. SBI will allow us to propagate complex selection effects directly from simulations to cosmological inference, relaxing some of the simplifying assumptions required by the standard likelihood approach. The comparison of both approaches on the same WL-selected sample will provide a stringent validation of the inferred cosmological constraints and a quantitative assessment of their robustness to modelling assumptions.