Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

Abstract

The integrated shear 3-point correlation function ζ ± measures the correlation between the local shear 2-point function ξ ± and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, ζ ± can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using ζ ±. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on N-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to ξ ±-only, adding ζ ± can lead to ≈ 10-25% improvements on the constraints of parameters like As (or σ 8) and w 0. We find no evidence in ξ ± + ζ ± constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where ζ ± is measured, and of the strategy to estimate the covariance matrix (N-body vs. lognormal). Our analysis solidifies the strong potential of the ζ ± statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data.