Information Gains in Cosmological Parameter Estimation

Fast cosmological parameter estimation using neural networks

We present a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called COSMONET, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released PICO algorithm of Fendt & Wandelt, but has several additional benefits in terms of ...

Parameter information from nonlinear cosmological fields

We develop a general formalism for analysing parameter information from nonGaussian cosmic fields. The method can be adapted to include the nonlinear effects in galaxy redshift surveys, weak lensing surveys and cosmic velocity field surveys as part of parameter estimation. It can also be used as a test of non-Gaussianity of the Cosmic Microwave Background. Generalising Maximum Likelihood analys...

Parameter estimation with additional information

Effects of CMB temperature uncertainties on cosmological parameter estimation

We estimate the effect of the experimental uncertainty in the measurement of the temperature of the cosmic microwave background (CMB) on the extraction of cosmological parameters from future CMB surveys. We find that even for an ideal experiment limited only by cosmic variance up to l = 2500 for both the temperature and polarisation measurements, the projected cosmological parameter errors are ...

Cosmological Parameter Estimation and the Spectral Index from Innation

Accurate estimation of cosmological parameters from microwave background aniso-tropies requires high-accuracy understanding of the cosmological model. Normally, a power-law spectrum of density perturbations is assumed, in which case the spectral index n can be measured to around 0:004 using microwave anisotropy satellites such as MAP and Planck. However, innationary models generically predict t...