Cosmological simulations for combined-probe analyses: covariance and neighbour-exclusion bias. Issue 1 (24th August 2018)

• Record Type: Journal Article
• Authors: Harnois-Déraps, J; Amon, A; Choi, A; Demchenko, V; Heymans, C; Kannawadi, A; Nakajima, R; Sirks, E; van Waerbeke, L; Cai, Yan-Chuan; Giblin, B; Hildebrandt, H; Hoekstra, H; Miller, L; Tröster, T
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 481:Issue 1(2018)
• Page Start: 1337
• Page End: 1367
• Publication Date: 2018-08-24
• Subjects: gravitational lensing: weak -- methods: numerical -- dark matter -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/sty2319
• Languages: English
• ISSNs: 0035-8711

Abstract

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 Kilo Degree Survey (KiDS)-450- and LSST-like lensing data, cosmic microwave background lensing maps and simulated spectroscopic surveys that emulate the Galaxy And Mass Assembly, BOSS, and 2-degree Field Lensing galaxy surveys. With 844 independent realizations, our mocks are optimized 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 Dark Energy Survey, where the rejection of neighbouring galaxies occurs within ∼2 arcsec, we show that the measured cosmic shear signal will be biased low, but by less than a per cent 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/ .