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Thesis
Home   /   Thesis   /   Bayesian Inference with Differentiable Simulators for the Joint Analysis of Galaxy Clustering and CMB Lensing

Bayesian Inference with Differentiable Simulators for the Joint Analysis of Galaxy Clustering and CMB Lensing

Artificial intelligence & Data intelligence Astrophysics Corpuscular physics and outer space Technological challenges

Abstract

The goal of this PhD project is to develop a novel joint analysis for the DESI galaxy clustering
and Planck PR4/ACT CMB lensing data, based on numerical simulations of the surveys and
state-of-the-art machine learning and statistical inference techniques. The aim is to overcome
many of the limitations of the traditional approaches and improve the recovery of cosmological
parameters. The joint galaxy clustering - CMB lensing inference will significantly improve
constraints on the growth of structure upon DESI-only analyses and refine even more the test of general relativity.

Laboratory

Institut de recherche sur les lois fondamentales de l’univers
Service de Physique des Particules
Groupe Cosmologie (GCOSMO)
Paris-Saclay
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