Lifting weak lensing degeneracies with a field-based likelihood

TL;DR

This paper introduces a field-based likelihood method for cosmic shear data analysis that jointly infers cosmological parameters and dark matter distribution, significantly reducing degeneracies and improving constraints over traditional two-point statistics.

Contribution

The novel field-based approach models the full matter distribution and initial fluctuations, extracting more information and tightening parameter constraints compared to standard methods.

Findings

Reduces degeneracy between $\

$ and $\

Abstract

We present a field-based approach to the analysis of cosmic shear data to infer jointly cosmological parameters and the dark matter distribution. This forward modelling approach samples the cosmological parameters and the initial matter fluctuations, using a physical gravity model to link the primordial fluctuations to the non-linear matter distribution. Cosmological parameters are sampled and updated consistently through the forward model, varying (1) the initial matter power spectrum, (2) the geometry through the distance-redshift relationship, and (3) the growth of structure and light-cone effects. Our approach extracts more information from the data than methods based on two-point statistics. We find that this field-based approach lifts the strong degeneracy between the cosmological matter density, $\Omega_\mathrm{m}$, and the fluctuation amplitude, $\sigma_8$, providing tight constraints on these parameters from weak lensing data alone. In the simulated four-bin tomographic experiment we consider, the field-based likelihood yields marginal uncertainties on $\sigma_8$ and $\Omega_\mathrm{m}$ that are, respectively, a factor of 3 and 5 smaller than those from a two-point power spectrum analysis applied to the same underlying data.