November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Unlocking fast cosmological parameter inference from Euclid with Marginal Neural Ratio Estimation

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster Paris Posters


Guillermo Franco Abellán (GRAPPA Institute, University of Amsterdam)


The Euclid space telescope will measure the shapes and redshifts of billions of galaxies, probing the growth of cosmic structures with an unprecedented precision. However, the increased quality of these data also means a significant increase in the number of nuisance parameters, making the cosmological inference a very challenging task. In this talk, I discuss the first application of Marginal Neural Ration Estimation (MNRE) (a recent approach in so-called simulation-based inference) to Euclid primary observables, like cosmic shear and galaxy-clustering spectra. Using expected Euclid experimental noise, I show how it’s possible to recover the posterior distribution for the cosmological parameters using an order of magnitude fewer simulations than conventional likelihood-based methods. This result supports that MNRE is a powerful framework to analyse Euclid data, allowing to extend the model complexity beyond what its currently achievable with standard MCMC.

Primary author

Guillermo Franco Abellán (GRAPPA Institute, University of Amsterdam)

Presentation materials