Non-gaussian forecasts of weak lensing with and without priors

• Verfasst von: Sellentin, Elena [VerfasserIn]
• Verfasst von: Schäfer, Björn Malte [VerfasserIn]
• Titel: Non-gaussian forecasts of weak lensing with and without priors
• Verf.angabe: Elena Sellentin and Björn Malte Schäfer
• Fussnoten: Gesehen am 08.11.2017
• Titel Quelle: Enthalten in: De.arxiv.org
• Jahr Quelle: 2015
• Band/Heft Quelle: (2015) Artikel-Nummer 1506.05356, 9 Seiten
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1565127943

Abstract

Assuming a Euclid-like weak lensing data set, we compare different methods of dealing with its inherent parameter degeneracies. 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 in order to enforce an approximately Gaussian likelihood. Here, we compare priorless forecasts to more conventional forecasts that use priors. We find strongly non-Gaussian likelihoods for 2d-weak lensing if no priors are used, which we approximate with the DALI-expansion. Without priors, the Fisher matrix of the 2d-weak lensing likelihood includes unphysical values of $\Omega_m$ and