Galaxy Power Spectrum Analysis: A Monte-Carlo Approach - Loureiro, Arthur Eduardo da Mota (2015)
Many galaxy surveys are planned to release their data over the next few years. Each different survey has its own geometrical limitations, which reflects upon the data as a selection function the spatial distribution of certain types of galaxies. Given a galaxy map (real or mock), the main goal of this work is to obtain information about how the selection function affects some of the cosmological parameters which can be probed from large-scale structure. A Monte-Carlo Markov Chain method is proposed in order to probe the effects of considering the selection functions parameters as nuisance parameters. The method consists in combining realizations of simulated galaxy catalogs using theoretical matter power spectra, combined with an optimal power spectrum estimator method. Theory and data are then compared in a multivariate Gaussian representing the likelihood function. This Monte-Carlo method has proven robust and capable of probing selection function effects on the cosmological parameters, showing that the simple marginalization over the nuisance parameters might lead to wrong estimates on the cosmology. The method is applied to obtain forecasts for these effects on the upcoming J-PAS Luminous Red Galaxies data and is employed to obtain constraints on the Hubble parameter (H0), the dark matter density (c) and two parameters of the equation of state of dark energy (w0 and wa).