5 citations · 6 across the 3 of their papers we have counts for
5 papers
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Zaccharie Ramzi, Benjamin Remy, Francois Lanusse +2
Deep neural networks have proven extremely efficient at solving a wide rangeof inverse problems, but most often the uncertainty on the solution they provideis hard to quantify. In…
Probabilistic Mapping of Dark Matter by Neural Score Matching
Benjamin Remy, Francois Lanusse, Zaccharie Ramzi +3
The Dark Matter present in the Large-Scale Structure of the Universe is invisible, but its presence can be inferred through the small gravitational lensing effect it has on the ima…
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
Niall Jeffrey, Justin Alsing, François Lanusse
In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates…
High Resolution Weak Lensing Mass-Mapping Combining Shear and Flexion
Francois Lanusse, Jean-Luc Starck, Adrienne Leonard +1
We propose a new mass-mapping algorithm, specifically designed to recover small-scale information from a combination of gravitational shear and flexion. Including flexion allows us…
Weak lensing reconstructions in 2D & 3D: implications for cluster studies
Adrienne Leonard, Francois Lanusse, Jean-Luc Starck
We compare the efficiency with which 2D and 3D weak lensing mass mapping techniques are able to detect clusters of galaxies using two state-of-the-art mass reconstruction technique…