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

Apr 3, 2023
21 pages
Published in:
  • JCAP 07 (2023) 040
  • Published: Jul 13, 2023
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Abstract: (IOP)
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 σ8_{8}) and w0_{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.
Note:
  • 21 pages, 11 figures, 3 tables. Comments welcome
  • weak gravitational lensing
  • cosmological parameters from LSS
  • Machine learning