This paper performs the first cosmological parameter analysis of the KiDS-1000 data with second- and third-order shear statistics. This work builds on a series of papers that describe the roadmap to third-order shear statistics. We derive and test a combined model of the second-order shear statistic, namely the COSEBIs and the third-order aperture mass statistics ⟨Map3⟩ in a tomographic set-up. We validate our pipeline with N-body simulations that mock the fourth Kilo Degree survey data release. To model the second- and third-order statistics, we use the latest version of \textsc{HMcode2020} for the power spectrum and \textsc{BiHalofit} for the bispectrum. Furthermore, we use an analytic description to model intrinsic alignments and hydro-dynamical simulations to model the effect of baryonic feedback processes. Lastly, we decreased the dimension of the data vector significantly by considering for the ⟨Map3⟩ part of the data vector only equal smoothing radii, making a data analysis of the fourth Kilo Degree survey data release using a combined analysis of COSEBIs third-order shear statistic possible. We first validate the accuracy of our modelling by analysing a noise-free mock data vector assuming the KiDS-1000 error budget, finding a shift in the maximum-a-posterior of the matter density parameter ΔΩm<0.02σΩm and of the structure growth parameter ΔS8<0.05σS8. Lastly, we performed the first KiDS-1000 cosmological analysis using a combined analysis of second- and third-order shear statistics, where we constrained Ωm=0.248−0.055+0.062 and S8=σ8Ωm/0.3=0.772±0.022. The geometric average on the errors of Ωm and S8 of the combined statistics increased compared to the second-order statistic by 2.2.
@article{arxiv.2309.08602,
title = {KiDS-1000 cosmology: Combined second- and third-order shear statistics},
author = {Pierre A. Burger and Lucas Porth and Sven Heydenreich and Laila Linke and Niek Wielders and Peter Schneider and Marika Asgari and Tiago Castro and Klaus Dolag and Joachim Harnois-Deraps and Konrad Kuijken and Nicolas Martinet},
journal= {arXiv preprint arXiv:2309.08602},
year = {2024}
}