Cosmologie

End-to-End Pipeline for CMB Polarization Analysis: From Time-Ordered Data to Cosmology, with a View Toward Simulation-Based Inference

This PhD project aims to develop a robust, unified data analysis pipeline for Cosmic Microwave Background (CMB) polarization experiments—starting from raw time-ordered data (TOD) and extending to cosmological parameter inference. Built upon the tools and frameworks developed in the ERC SciPol project (e.g. FURAX, MegaTop), the work will model complex systematics such as instrumental noise, beam mismatches, and foregrounds. In the later stages, the project may explore Simulation-Based Inference (SBI) techniques to bypass traditional likelihood approximations, enabling direct inference of cosmological parameters (e.g. tensor-to-scalar ratio r, birefringence angle) from forward simulations. The PhD will combine high-performance simulation, probabilistic modeling (using JAX), and CMB pipeline development, in collaboration with the SciPol, Simons Observatory, and LiteBIRD teams.

Bayesian pipelines for cosmic shear analyses with the Vera Rubin Observatory LSST

3-year PhD thesis starting with a 3-month inter

DESI-2 high-redshift survey preparation: Lyman-Break Galaxies and Lyman Alpha Emitters

This internship is in the context of the DESI-2 (2029-2035) experiment preparation.

Transformer-based methods for cluster detection in astronomical images

Deep Learning techniques have revolutionized artificial intelligence. Their application to astrophysics and cosmology permits us to analyze the large quantity of data obtained with current surveys and expected from future surveys with the aim of improving our understanding of the cosmological model.

Machine Learning for Photometric redshift estimation of LSST galaxies

Machine Learning techniques have revolutionized artificial intelligence. Their application to astrophysics and cosmology permits us to analyze the large quantity of data obtained with current surveys and expected from future surveys with the aim of improving our understanding of the cosmological model.

In Memory -- George Fitzgerald Smoot, III

The APC Laboratory mourns the loss of George Smoot, who passed away unexpectedly at his home in Paris. George Smoot was one of the pioneers of observations of the cosmic microwave background (CMB), which revolutionized our understanding of the cosmos and placed cosmology on a firm experimental footing. (Credits photo: Peter Badge/Lindau Nobel Laureate Meetings)

En mémoire de Yannick Giraud-Héraud

C’est avec une profonde tristesse que nous avons appris le décès de notre collègue et ami Yannick Giraud-Héraud survenu le 13 juin 2025.

SEARCHING FOR PRIMORDIAL GRAVITATIONAL WAVES WITH QUBIC: High Precision Calibration using Interferometry

The quest for B-mode polarization of the Cosmic Microwave Background is the primary challenge in Observational Cosmology and is pursued by a worldwide effort.

Introduction à l'analyse des données du fond diffus cosmologique

Ce stage offre une initiation aux techniques d'analyse des données astrophysiques liées au fond diffus cosmologique (CMB). Il permettra à l’étudiant.e de se familiariser avec les méthodes utilisées pour explorer et interpréter les informations contenues dans ces observations provenant de l'Univers primordial. Le projet se concentrera en particulier sur l’optimisation des techniques dites de séparation de composantes, qui distinguent le CMB des contaminations galactiques.

Creating Probabilistic Catalogues from Deblended Galaxies

Dans le cadre de la préparation des données du télescope LSST de l’observatoire Vera C. Rubin qui fear sa première lumière en 2025, l’un des défis majeurs est le deblending des galaxies, c’est-à-dire la séparation des contributions lumineuses de galaxies superposées dans les images astronomiques.

Cosmologie