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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…
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
Niall Jeffrey, Benjamin D. Wandelt
High-dimensional probability density estimation for inference suffers from the "curse of dimensionality". For many physical inference problems, the full posterior distribution is u…
The sum of the masses of the Milky Way and M31: a likelihood-free inference approach
Pablo Lemos, Niall Jeffrey, Lorne Whiteway +3
We use Density Estimation Likelihood-Free Inference, Cold Dark Matter simulations of galaxy pairs, and data from Gaia and the Hubble Space Telescope to infer the sum…
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…