Cosmologie

Le Laboratoire Astroparticule & Cosmologie accueille une importante délégation ministérielle franco-polonaise

Le 3 décembre 2025, le Laboratoire Astroparticule & Cosmologie a accueilli une délégation polonaise dirigée par Prof. Andrzej Szeptycki, sous-secrétaire d’État au ministère polonais des Sciences et de l’Enseignement supérieur. Cette visite s’inscrivait dans le cadre du 8e Forum franco-polonais pour la science et l’innovation. Elle intervient à un moment charnière : la signature d’un accord entre le CNRS et l’Académie des Sciences de Pologne, visant à renforcer le partenariat entre l’APC et le futur International Institute for Particle Astrophysics (AstroCeNT).

Cosmic Census

Cosmology boasts a successful standard cosmological model. While its success rests on its ability to explain diverse observations, its practical use derives from its framing of open questions. One central open question is: How does the cosmic ecosystem of dark matter and baryons co-evolve? The thesis research will examine this question by modeling the cross-correlation of the Euclid mission’s galaxy and lensing survey with cosmic microwave background (CMB) surveys - the Atacama Cosmology Telescope (ACT), the South Pole Telescope (SPT), and the Simons Observatory (SO).

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.

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)
Cosmologie