Our mission

We are an international collaboration of researchers interested in developing and applying cutting-edge statistical inference techniques to study the spatial distribution of matter in our Universe. We embrace the latest innovations in information theory and artificial intelligence to optimally extract physical information from data and use derived results to facilitate new discoveries.

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Our latest results

Fifth force picture

Fifth force on galaxy cluster scale

The tightest bound on fifth-force class of modified gravity models are found on the basis of the BORG-PM model and innovative small scale modelling of the gravitational field. Those bounds even hint at possible positive detection, though more investigation on the impact of galaxy physics is required to assess this claim.

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Precision cosmology with expansion

We have developed a novel algorithm to infer cosmological constraints within a large-scale Bayesian inference framework. This hierarchical approach, relying purely on the geometrical symmetries of the cosmological principle, is among the first methods to extract a large fraction of information from statistics other than that of direct density contrast correlations.

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Bayesian treatment of unknown foregrounds

We present an effective solution to the unknown foreground contamination problem in galaxy survey analyses. We have developed a robust likelihood designed to account for effects due to unknown foreground and target contaminations by effectively marginalizing over the unknown large-scale contamination amplitudes.

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Funding partners

We currently receive individual funding provided by the French ANR (BIG4 ANR-16-CE23-0002), the ERC, the Institut Lagrange de Paris (ANR-10-LABX-63, ANR-11-IDEX-0004-02), the CNRS, the Max Planck Institute for Astrophysics, the Imperial College.