Context

Formed hierarchically from the collapse of initial density fluctuations, the evolution of galaxy clusters over time reflects the underlying cosmology of the Universe. The measurement of their abundance, i.e., the number of clusters as a function of mass and redshift, can provide constraints on dark energy and structure growth (e.g. Ghirardini et al. 2024, Artis et al. 2025)

Up to now, cosmological constraints from cluster abundance rely on cluster detection through their baryonic signal (e.g. through the intracluster gas content in X-rays, using the Sunyaev-Zeldovich effect at millimetre wavelengths, or through the optical light in the galaxies). However, the baryonic selection is known to suffer from several biases, primarily linked to feedback processes within the cluster galaxies. 

In this project, we propose to follow the pioneering work of Chiu I-N. et al 2024 on Hyper SuprimeCam data that derived first cosmological constraints based on 129 clusters detected through their lensing signal i.e. directly through their total projected mass. Using the sensitivity of the ESA’s Euclid mission combined with its sky coverage, we will build a unique weak-lensing selected cluster catalogue that will be a valuable asset for cluster studies due to its size and selection characteristics. We expect to detect more than 2000 clusters with the first year Euclid data release (DR1) alone, and more than 15 000 clusters with the final survey data. Exploitation of these data to obtain cosmological constraints requires the development of new approaches, notably in the characterisation of the relation between the observable signal and the underlying mass, and the associated selection function. 

Research project

The thesis project aims at building and characterising a sample of galaxy clusters detected by weak lensing from DR1 Euclid data to provide new constraints on galaxy cluster abundances for cosmological analysis.

In the first part of the thesis the candidate will build a least-contaminated sample of galaxy clusters detected by weak lensing from Euclid data and will characterise its selection function. Over the past several years, we have developed a multi-scale cluster detection method applicable to Euclid weak lensing data. The work will involve optimising these lensing-based algorithms to detect clusters in Euclid DR1 data and build a sample with minimal contamination. The selection function will then be computed by injecting the shear signal of synthetic clusters in the real Euclid weak-lensing mass maps (e.g. Chen et al 2024).

The second part of the thesis will focus on characterising the measurement uncertainty of the weak-lensing observable and establish its relation with the underlying halo mass. The bias and scatter of this relation will be calibrated against realistic halos in cosmological simulations as done in Grandis et al. 2021.

In the last part  of the thesis the candidate will derive cosmological constraints from the modelling of the cluster abundance using classical modelling (e.g. Chiu et al 2024). Simulation-based Inference methods will also be explored (e.g. Cerardi et al. 2025).

The thesis will be conducted in a very stimulating context as the candidate will have access to the first year Euclid data from the outset.

Scientific environment

The thesis will take place at the interface of LILAS and LCEG groups in IRFU CEA Saclay that have respectively an expertise in signal processing and Galaxy Clusters. The supervision of the thesis will be jointly performed by Sandrine Pires (Astrostatistician and Weak Lensing expert) and Gabriel W. Pratt (Galaxy Cluster expert). S. Pires is a member of Euclid collaboration. She is in charge of the production of the Weak Lensing Cluster catalogue and its scientific exploitation. G.W. Pratt is a PI of the CHEX-MATE project, in addition to being a full member of Euclid and NewAthena. The thesis will be carried out in close collaboration with Laura Salvati (Cluster cosmology expert, IAS).

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For more Information about the topics and the co-financial partner (found by the lab!); contact Directeur de thèse - sandrine.pires@cea.fr

Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online before March 13th, 2026 Midnight Paris time!