Non-gaussian forecasts of weak lensing with and without priors
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...
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| Main Authors: | , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
18 June 2015
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| In: |
Arxiv
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| Online Access: | Verlag, kostenfrei, Volltext: http://arxiv.org/abs/1506.05356
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| Author Notes: | Elena Sellentin and Björn Malte Schäfer |
| Summary: | 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 |
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| Item Description: | Gesehen am 08.11.2017 |
| Physical Description: | Online Resource |