17 citations · 34 across the 3 of their papers we have counts for
8 papers
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference
Denise Lanzieri, Justine Zeghal, T. Lucas Makinen +3
Traditionally, weak lensing cosmological surveys have been analyzed using summary statistics motivated by their analytically tractable likelihoods, or by their ability to access hi…
Learning Diffusion Priors from Observations by Expectation Maximization
François Rozet, Gérôme Andry, François Lanusse +1
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, whi…
pmwd: A Differentiable Cosmological Particle-Mesh -body Library
Yin Li, Libin Lu, Chirag Modi +7
The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentia…
CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines
Chirag Modi, François Lanusse, Uroš Seljak +2
Reconstructing the Gaussian initial conditions at the beginning of the Universe from the survey data in a forward modeling framework is a major challenge in cosmology. This require…
An information-based metric for observing strategy optimization, demonstrated in the context of photometric redshifts with applications to cosmology
Alex I. Malz, François Lanusse, John Franklin Crenshaw +1
The observing strategy of a galaxy survey influences the degree to which its resulting data can be used to accomplish any science goal. LSST is thus seeking metrics of observing st…