Describing variations of the Fisher-matrix across parameter space

Forecasts in cosmology, both with Monte Carlo Markov-chain methods and with the Fisher-matrix formalism, depend on the choice of the fiducial model because both the signal strength of any observable and the model non-linearities linking observables to cosmological parameters vary in the general case...

Full description

Saved in:
Bibliographic Details
Main Authors: Schäfer, Björn Malte (Author) , Reischke, Robert (Author)
Format: Article (Journal)
Language:English
Published: 20 May 2016
In: Monthly notices of the Royal Astronomical Society
Year: 2016, Volume: 460, Issue: 3, Pages: 3398-3406
ISSN:1365-2966
DOI:10.1093/mnras/stw1221
Online Access:Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1093/mnras/stw1221
Get full text
Author Notes:Björn Malte Schäfer and Robert Reischke
Description
Summary:Forecasts in cosmology, both with Monte Carlo Markov-chain methods and with the Fisher-matrix formalism, depend on the choice of the fiducial model because both the signal strength of any observable and the model non-linearities linking observables to cosmological parameters vary in the general case. In this paper we propose a method for extrapolating Fisher-forecasts across the space of cosmological parameters by constructing a suitable basis. We demonstrate the validity of our method with constraints on a standard dark energy model extrapolated from a ΛCDM-model, as can be expected from two-bin weak lensing tomography with an Euclid-like survey, in the parameter pairs (Ωm, σ8), (Ωm, w0) and (w0, wa). Our numerical results include very accurate extrapolations across a wide range of cosmological parameters in terms of shape, size and orientation of the parameter likelihood, and a decomposition of the change of the likelihood contours into modes, which are straightforward to interpret in a geometrical way. We find that in particular the variation of the dark energy figure of merit is well captured by our formalism.
Item Description:Gesehen am 06.11.2016
Physical Description:Online Resource
ISSN:1365-2966
DOI:10.1093/mnras/stw1221