Cosmological Simulations for Combined-Probe Analyses: Covariance and Neighbour-Exclusion Bias

TL;DR

This paper introduces a comprehensive suite of simulated weak lensing data for combined cosmological probe analyses, enabling covariance estimation, systematic testing, and bias assessment, especially for neighbour-exclusion effects in upcoming surveys.

Contribution

The authors provide a publicly available set of high-resolution mock data tailored for combined-probe covariance estimation and systematic bias testing in weak lensing surveys.

Findings

Neighbour-exclusion bias causes less than 1% bias in KiDS-like data.

Bias doubles in LSST-like deeper surveys.

Mocks are optimized for covariance estimation and systematic validation.

Abstract

We present a public suite of weak lensing mock data, extending the Scinet Light Cone Simulations (SLICS) to simulate cross-correlation analyses with different cosmological probes. These mocks include KiDS-450- and LSST-like lensing data, cosmic microwave background lensing maps and simulated spectroscopic surveys that emulate the GAMA, BOSS and 2dFLenS galaxy surveys. With 844 independent realisations, our mocks are optimised for combined-probe covariance estimation, which we illustrate for the case of a joint measurement involving cosmic shear, galaxy-galaxy lensing and galaxy clustering from KiDS-450 and BOSS data. With their high spatial resolution, the SLICS are also optimal for predicting the signal for novel lensing estimators, for the validation of analysis pipelines, and for testing a range of systematic effects such as the impact of neighbour-exclusion bias on the measured tomographic cosmic shear signal. For surveys like KiDS and DES, where the rejection of neighbouring galaxies occurs within ~2 arcseconds, we show that the measured cosmic shear signal will be biased low, but by less than a percent on the angular scales that are typically used in cosmic shear analyses. The amplitude of the neighbour-exclusion bias doubles in deeper, LSST-like data. The simulation products described in this paper are made available at http://slics.roe.ac.uk/.