Cosmology from Clustering, Cosmic Shear, CMB Lensing, and Cross Correlations: Combining Rubin Observatory and Simons Observatory

TL;DR

This paper demonstrates how combining Rubin Observatory and Simons Observatory data enhances cosmological parameter constraints, introduces a new mitigation method for photo-z outliers, and emphasizes the importance of joint analysis for future surveys.

Contribution

It presents a simulated joint likelihood analysis of LSST and SO data, introduces the island model for photo-z outlier mitigation, and quantifies the benefits of combined datasets.

Findings

Dark energy FoM increases by over 50% with combined data.

Using the same galaxy sample for clustering and lensing improves signal-to-noise by 30-40%.

The island model effectively reduces biases from catastrophic photo-z outliers.

Abstract

In the near future, the overlap of the Rubin Observatory Legacy Survey of Space and Time (LSST) and the Simons Observatory (SO) will present an ideal opportunity for joint cosmological dataset analyses. In this paper we simulate the joint likelihood analysis of these two experiments using six two-point functions derived from galaxy position, galaxy shear, and CMB lensing convergence fields. Our analysis focuses on realistic noise and systematics models and we find that the dark energy Figure-of-Merit (FoM) increases by 53% (92%) from LSST-only to LSST+SO in Year 1 (Year 6). We also investigate the benefits of using the same galaxy sample for both clustering and lensing analyses, and find the choice improves the overall signal-to-noise by ~30-40%, which significantly improves the photo-z calibration and mildly improves the cosmological constraints. Finally, we explore the effects of catastrophic photo-z outliers finding that they cause significant parameter biases when ignored. We develop a new mitigation approach termed "island model", which corrects a large fraction of the biases with only a few parameters while preserving the constraining power.