Laboratory / university: CRIStAL (UMR 9189), SigMA team, University of Lille
Duration: 36 months
Co-supervisors:
- P. Chainais pierre.chainais@univ-lille.fr
- Jenny Sorce jenny.sorce@univ-lille.fr
- P.-A. Thouvenin pierre-antoine.thouvenin@centralelille.fr
Context:
This PhD thesis proposal focuses on improving galaxy cluster mass estimation to enhance cosmological parameter inference, in order to address critical challenges such as the S8 cosmological parameter discrepancy. By leveraging multi-wavelength observations, advanced statistical techniques including Bayesian inference and machine learning to solve this inverse problem, the project aims to refine relations between the cluster mass and their observable properties, reduce data dimensionality, and efficiently process large datasets. Anticipated outcomes include innovative Bayesian hierarchical models, optimized data reduction algorithms, and a robust inference pipeline for generating a high-fidelity galaxy cluster mass catalog towards testing cosmological models.
Subject:
The standard cosmological model successfully describes the Universe’s large-scale structure and evolution. Observational evidence, including the cosmic microwave background (CMB) and the hierarchical distribution of galaxies in the cosmic web, provides strong support for this model. These data suggest that dark matter and dark energy dominate the Universe, together constituting approximately 95% of its total content.
Despite its success, this model faces challenges. For instance, the value derived for the S8 parameter, which characterizes the matter distribution on certain scales, differs by three standard deviations when using galaxy cluster counts instead of CMB observations. Resolving this tension is critical to determine whether new physics theories are required, or systematic errors in data analysis pipelines need to be accounted for.
Galaxy clusters are a key tool to address these challenges. As the largest gravitationally bound structures in the Universe, their number and masses are closely related to the underlying cosmology. Accurately estimating their masses and number across diverse environments and redshifts is essential to test the standard model and refine our understanding of the Universe.
This PhD project focuses on developing Bayesian hierarchical models to improve the estimation of galaxy cluster masses. By leveraging multi-wavelength observations, advanced data reduction, and machine learning techniques, this research will provide more precise mass estimates, refine scaling relations, and facilitate the inference of cosmological parameters from large datasets.
Candidate profile: Master 2 or last year engineering school students with major in applied mathematics, computer science or electrical engineering. The project requires a strong background in data science and/or machine learning (statistics, optimization), signal & image processing. Very good Python coding skills are expected. A B2 English level minimum is mandatory.
Application procedure: applicants are invited to send the following documents as a single file in .pdf format to all the co-supervisors:
- a detailed curriculum;
- a cover letter;
- official transcripts and certificates of graduation from the institutions you have attended over the last 3
years (in French or in English); - references: letters of recommendation or names of two researchers/professors willing to recommend your application.
