Combining information from multiple cosmological surveys: inference and modeling challenges

TL;DR

Combining multiple cosmological surveys enhances the robustness of results but introduces complex modeling and inference challenges that require coordinated efforts across different datasets and analysis frameworks.

Contribution

This paper highlights the importance of integrated analysis and modeling strategies for multiple surveys to improve cosmological inference accuracy.

Findings

Cross-survey data combination improves cosmological constraints.

Homogenizing analysis frameworks facilitates better systematic error control.

Coordinated modeling efforts are essential for future large-scale surveys.

Abstract

The tightest and most robust cosmological results of the next decade will be achieved by bringing together multiple surveys of the Universe. This endeavor has to happen across multiple layers of the data processing and analysis, e.g., enhancements are expected from combining Euclid, Rubin, and Roman (as well as other surveys) not only at the level of joint processing and catalog combination, but also during the post-catalog parts of the analysis such as the cosmological inference process. While every experiment builds their own analysis and inference framework and creates their own set of simulations, cross-survey work that homogenizes these efforts, exchanges information from numerical simulations, and coordinates details in the modeling of astrophysical and observational systematics of the corresponding datasets is crucial.