Non-Gaussian forecasts of weak lensing with and without priors

作者：Sellentin Elena^*; Schaefer Bjoern Malte

来源：Monthly Notices of the Royal Astronomical Society, 2016, 456(2): 1645-1653.

DOI：10.1093/mnras/stv2805

• gravitational lensing: weak
• methods: data analysis

Including priors into a data analysis can mask the information content of a given data set alone. However, since the information content of a data set is usually estimated with the Fisher matrix, priors are added to enforce an approximately Gaussian likelihood. Here, we estimate the information content of a Euclid-like weak lensing data set with and without priors. Without priors, the Fisher matrix for 2d-weak lensing includes unphysical values of Omega(m) and h. The Cramer-Rao inequality then does not need to apply. We find that the new DALI expansion and Monte Carlo Markov Chains agree well and predict the presence of a dark energy with high significance, whereas a Fisher forecast also allows decelerated expansion. We find that a 2d-weak lensing analysis provides a sharp lower limit on the Hubble constant of h > 0.4, even if the equation of state of dark energy is jointly estimated. This is not predicted by the Fisher matrix and usually masked in other works by a sharp prior on h. Additionally, we find that DALI estimates Figures of Merit in the presence of non-Gaussianities better than the Fisher matrix and demonstrate how DALI allows switching to a speedy Hamiltonian Monte Carlo sampling of a highly curved likelihood with acceptance rates of approximate to 0.5. This shows how quick forecasts can be upgraded to accurate forecasts whenever needed. Results were gained with the public code from DALI.

• 出版日期2016-2-21