Map-based cosmology inference with weak lensing – information content and its dependence on the parameter space

Abstract

ABSTRACT Field-level inference is emerging as a promising technique for optimally extracting information from cosmological data sets. Previous analyses have shown field-based inference produces tighter parameter constraints than power spectrum analyses. However, estimates of the detailed quantitative gain in constraining power differ. Here, we demonstrate the gain in constraining power depends on the parameter space being constrained. As a specific example, we find that lognormal field-based analysis of an LSST Y1-like mock data set only marginally improves constraints relative to a 2-point function analysis in Lambda cold dark matter (ΛCDM), yet it more than doubles the constraining power of the data in the context of wCDM models. This effect reconciles some, but not all, of the discrepant results found in the literature. Our results suggest the importance of using a full systematics model when quantifying the information gain for realistic field-level analyses of future data sets.