Forecasting cosmological parameter bias introduced by intrinsic alignment with the halo model

Abstract

Cosmic shear is the correlation of the distorted shapes of distant galaxies due to weak gravitational lensing by the large-scale structure (LSS) of the Universe. By measuring cosmic shear, we can study the distribution of matter and the evolution of structure on large scales. It is seen as a promising tool to constrain cosmological parameters. Galaxy intrinsic alignments (IA) are an important contaminant for cosmic shear studies. Modelling of the intrinsic alignments is required to accurately extract the lensing signal and constrain cosmological parameters. The halo model attempts to better describe intrinsic alignment at small scales by including a 1-halo term that models close-range correlations between satellite galaxies. By extending our Fisher forecast with the bias vector formalism, we are able to determine the bias introduced by mismodelling halo model alignments with the NLA model. We initially find significant parameter biases, but these disappear when we include a window function to the halo model power spectra to boost the intermediate scales. This suggests that small scales don’t seem to affect the parameter biases nearly as much as the intermediate scales. We expect that this work should motivate further research into correctly modelling the intermediate scales.