Modeling the impacts of galaxy intrinsic alignments on weak lensing peak statistics

TL;DR

This paper develops a theoretical model to quantify and mitigate the impact of galaxy intrinsic alignments on weak lensing peak statistics, enhancing the precision of cosmological parameter estimation from upcoming surveys.

Contribution

It extends previous halo-based models by incorporating intrinsic alignment effects, validated with simulations, and demonstrates how to constrain IA parameters using peak statistics.

Findings

Model accurately predicts IA effects on WL peaks for 5b0 dispersions.

Neglecting IA can bias b0 in b0 surveys.

Model enables simultaneous mitigation of IA bias and constrains satellite IA to b124b0.

Abstract

Weak gravitational lensing (WL) peak statistics capture cosmic non-linear structures and can provide additional cosmological information complementary to cosmic shear two-point correlation analyses. They have been applied to different WL surveys successfully. To further facilitate their high precision applications, it is very timely to investigate the impacts of different systematics on WL peak statistics and how to mitigate them. Concerning the influence from galaxy intrinsic alignments (IAs), in this paper, we develop a theoretical model for WL high peaks taking into account the IA effects. It is an extension of our previous halo-based model. The IA corrections mainly include the modification of the lensing profile of clusters of galaxies due to the alignments of satellite galaxies and the additional shape noise correlations. We validate our model using simulations with the semi-analytical galaxy formation. We consider the cases where the satellite galaxies are averagely radially aligned toward the centers of their host clusters but with different dispersions $\sigma_{\theta}$. We show that our model works well for $\sigma_{\theta}>45^{\circ}$. If the IA corrections are not included in the model, for the Euclid/CSST-like source galaxy distribution and the survey area of $\sim 1000 deg^2$, the IA induced bias on $S_8$ can reach $\sim 8\sigma$ even for $\sigma_{\theta}=75^{\circ}$. With our model, not only the bias can be well mitigated, but also we can constrain the satellite IA to the level of {\bf $\sigma(\sigma_{\theta})\sim \pm 24^{\circ}$} simultaneously from WL high peak analyses alone using data from such a survey.