KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing

A critical review is given of the current status of cosmological nucleosynthesis. In the framework of the Standard Model with 3 types of relativistic neutrinos, the baryon-to-photon ratio, \(\eta\), corresponding to the inferred primordial abundances of deuterium and helium-4 is consistent with the independent determination of \(\eta\) from observations of anisotropies in the cosmic microwave background. However the primordial abundance of lithium-7 inferred from observations is significantly below its expected value. Taking systematic uncertainties in the abundance estimates into account, there is overall concordance in the range \(\eta = (5.7-6.7)\times 10^{-10}\) at 95% CL (corresponding to a cosmological baryon density \(\Omega_B h^2 = 0.021 - 0.025\)). The D and He-4 abundances, when combined with the CMB determination of \(\eta\), provide the bound \(N_\nu=3.28 \pm 0.28\) on the effective number of neutrino species. Other constraints on new physics are discussed briefly.

We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent CMB experiments and provide parameter constraints, including sigma_8, from the CMB independent of other data. We next combine data from the CMB, HST Key Project, 2dF galaxy redshift survey, supernovae Ia and big-bang nucleosynthesis. The Monte Carlo method allows the rapid investigation of a large number of parameters, and we present results from 6 and 9 parameter analyses of flat models, and an 11 parameter analysis of non-flat models. Our results include constraints on the neutrino mass (m_nu < 0.3eV), equation of state of the dark energy, and the tensor amplitude, as well as demonstrating the effect of additional parameters on the base parameter constraints. In a series of appendices we describe the many uses of importance sampling, including computing results from new data and accuracy correction of results generated from an approximate method. We also discuss the different ways of converting parameter samples to parameter constraints, the effect of the prior, assess the goodness of fit and consistency, and describe the use of analytic marginalization over normalization parameters.