Response approach to the integrated shear 3-point correlation function: the impact of baryonic effects on small scales

Abstract

The integrated shear 3-point correlation function 𝜁 ± is a higher-order statistic of the cosmic shear field that describes the modulation of the 2-point correlation function 𝜉 ± by long-wavelength features in the field. Here, we introduce a new theoretical model to calculate 𝜁 ± that is accurate on small angular scales, and that allows to take baryonic feedback effects into account. Our model builds on the realization that the small-scale 𝜁 ± is dominated by the nonlinear matter bispectrum in the squeezed limit, which can be evaluated accurately using the nonlinear matter power spectrum and its first-order response functions to density and tidal field perturbations. We demonstrate the accuracy of our model by showing that it reproduces the small-scale 𝜁 ± measured in simulated cosmic shear maps. The impact of baryonic feedback enters effectively only through the corresponding impact on the nonlinear matter power spectrum, thereby permitting to account for these astrophysical effects on 𝜁 ± similarly to how they are currently accounted for on 𝜉 ± . Using a simple idealized Fisher matrix forecast for a DES-like survey we find that, compared to 𝜉 ± , a combined 𝜉 ± & 𝜁 ± analysis can lead to improvements of order 20 − 40% on the constraints of cosmological parameters such as 𝜎 8 or the dark energy equation of state parameter 𝑤 0 . We find similar levels of improvement on the constraints of the baryonic feedback parameters, which strengthens the prospects for cosmic shear data to obtain tight constraints not only on cosmology but also on astrophysical feedback models. These are encouraging results that motivate future works on the integrated shear 3-point correlation function towards applications to real survey data.

Comparing weak lensing peak counts in baryonic correction models to hydrodynamical simulations

“…However, it has been found that these statistics do not capture all the information from the non-linear and non-Gaussian regime of the WL field. This has led to an array of work attempting to extract this additional information, including non-Gaussian 𝑁-point statistics such as the 3-point function and its Fourier transform the angular bispectrum (Fu et al 2014;Coulton et al 2019;Halder et al 2021;Halder & Barreira 2022), Minkowski functionals (Maturi et al 2010;Munshi et al 2011;Kratochvil et al 2012;Petri et al 2013;Marques et al 2019;Gatti et al 2021), convergence peak and minima statistics (Jain & Van Waerbeke 2000;Yang et al 2011;Liu et al 2015;Kacprzak et al 2016;Li et al 2019;Martinet et al 2021;Zürcher et al 2021;Harnois-Déraps et al 2021), as well as machine learning methods (Gupta et al 2018;Fluri et al 2019;Ribli et al 2019;Matilla et al 2020;) and related techniques (Cheng et al 2020).…”