Cosmologie

Simulation-Based Inference for CMB B-Modes in a Minimal End-to-End Toy Pipeline

Measurements of the Cosmic Microwave Background (CMB) polarization are among the most promising avenues for detecting primordial gravitational waves through the tensor-to-scalar ratio r. However, classical analysis pipelines (time-ordered data → maps → spectra → likelihood) rely on numerous approximations and treat instrumental noise, foregrounds, and cosmological signals in separate stages. Simulation-Based Inference (SBI) provides an alternative approach: instead of assuming an explicit likelihood, one trains neural estimators on simulations and infers posteriors directly from data. This strategy is central to the ERC SciPol project, which aims to build next-generation end-to-end CMB analysis tools.

Thèses 2026 : découvrez les sujets proposés par l’APC

Le laboratoire AstroParticule et Cosmologie (APC) ouvre ses portes aux futur·e·s doctorant·e·s. Les sujets de thèse pour 2026, portés par les équipes de recherche du laboratoire, sont désormais disponibles ici et couvrent un large spectre de thématiques associées à la physique des deux infinis : astrophysique des hautes énergies, cosmologie, physique des particules, ondes gravitationnelles et physique théorique. Les candidat·e·s intéressé·e·s sont invité·e·s à prendre contact avec la personne référente indiquée pour chaque proposition.

Le Laboratoire Astroparticule & Cosmologie accueille une 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.

Cosmologie