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...
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...
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.
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...
