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

TL;DR

This paper investigates how the effectiveness of field-level inference in cosmology varies with the parameter space, showing it can significantly improve constraints in extended models like wCDM but only marginally in standard ΛCDM.

Contribution

It demonstrates that the information gain from field-based inference depends on the cosmological model, highlighting the importance of systematics modeling for future data analysis.

Findings

Field-based inference offers limited improvement over 2-point functions in ΛCDM.

In wCDM models, field-based analysis more than doubles the constraining power.

The effectiveness of inference methods varies with the chosen cosmological parameter space.

Abstract

Field-level inference is emerging as a promising technique for optimally extracting information from cosmological datasets. Indeed, 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 field-based analysis of an LSST Y1-like mock data set only marginally improves constraints relative to a 2-point function analysis in $\Lambda$CDM, yet it more than doubles the constraining power of the data in the context of $w$CDM models. This effect reconciles some, but not all, of the discrepant results found in the literature. Our results demonstrate the importance of using a full systematics model when quantifying the information gain for realistic field-level analyses of future data sets.