Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics 3rd ed.

Session

Bayesian inferences

May 22, 2025, 9:00 AM

Buffon Amphitheater (Université Paris Cité)

15. Neural compression and neural density estimation for cosmological inference

Justine Zeghal (APC)

5/22/25, 9:00 AM

Talk

keynote talk + discussion session

4. Benchmarking field-level cosmological inference from galaxy redshift surveys

Hugo Simon (CEA Paris-Saclay)

5/22/25, 10:50 AM

Cosmology

Talk

Field-level inference has emerged as a promising framework to fully harness the cosmological information encoded in next-generation galaxy surveys. It involves performing Bayesian inference to jointly estimate the cosmological parameters and the initial conditions of the cosmic field, directly from the observed galaxy density field. Yet, the scalability and efficiency of sampling algorithms...

30. Bayesian inverse problem with scattering transform : application to instrumental decontamination

Sebastien PIERRE (LPENS)

5/22/25, 11:10 AM

Talk

Decontaminating a signal of interest is a recurring challenge in astrophysics and cosmology. Given the stochastic nature of usual contaminations (for instance instrumental, or from cosmological background or Galactic foregrounds), it can be framed as an ill-posed inverse problem. A Bayesian approach is needed to recover a distribution of signals compatible with the observed data. We propose a...

32. CMBAgent -- Open Source Planning and Control System for Science with Large Language Model Agents

Boris Bolliet (Cambridge)

5/22/25, 11:30 AM

Cosmology

Talk

We may be on the cusp of a paradigm shift in scientific research, where hypotheses, experiments, and interpretations are autonomously generated and implemented by multi-agent AI systems. We will present recent developments in Cosmology and Astrophysics where early prototypes of such systems are already being deployed on cutting-edge observational and simulation datasets.

25. What does it take to make MCMC feasible in very high dimensions?

Jakob Robnik (University of California at Berkeley)

5/22/25, 11:50 AM

ML Methodology

Talk

Sampling from high-dimensional distributions is an important tool in Bayesian inference problems, like cosmological field level inference and Bayesian neural networks (BNN).
Hamiltonian Monte Carlo and its tuning-free implementation NUTS have pushed the limits of typical dimensionalities where sampling is feasible. I will show that this limit can be pushed further by disposing of the...

24. JaxPM: A JAX-Based Framework for Scalable and Differentiable Particle Mesh Simulations in Cosmology

Wassim KABALAN (CNRS APC/IN2P3)

5/22/25, 12:10 PM