102 citations · 303 across the 13 of their papers we have counts for
13 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…
JAX-COSMO: An End-to-End Differentiable and GPU Accelerated Cosmology Library
Jean-Eric Campagne, François Lanusse, Joe Zuntz +7
We present jax-cosmo, a library for automatically differentiable cosmological theory calculations. It uses the JAX library, which has created a new coding ecosystem, especially in…
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…
Differentiable Cosmological Simulation with Adjoint Method
Yin Li, Chirag Modi, Drew Jamieson +5
Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic…
The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
Marc Huertas-Company, François Lanusse
The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. Extracting coherent scientific information from these large a…
From Data to Software to Science with the Rubin Observatory LSST
Katelyn Breivik, Andrew J. Connolly, K. E. Saavik Ford +97
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the…
Neural Posterior Estimation with Differentiable Simulators
Justine Zeghal, François Lanusse, Alexandre Boucaud +2
Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances…
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…
Deep learning dark matter map reconstructions from DES SV weak lensing data
Niall Jeffrey, François Lanusse, Ofer Lahav +1
We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based arch…
Sparse reconstruction of the merging A520 cluster system
Austin Peel, François Lanusse, Jean-Luc Starck
Merging galaxy clusters present a unique opportunity to study the properties of dark matter in an astrophysical context. These are rare and extreme cosmic events in which the bulk…