Anisotropic Halo Model

TL;DR

This paper extends the halo model to include triaxial shapes and alignments, deriving anisotropic cross-correlations and comparing them with simulations to understand dark matter halo alignments in the b4CDM framework.

Contribution

It introduces a triaxial, anisotropic halo model with GPU-accelerated Monte Carlo integration for precise comparison with simulations.

Findings

Quantifies dark matter halo alignments using anisotropic correlations.

Demonstrates the effectiveness of GPU-based Monte Carlo methods for complex integrals.

Provides a framework for analyzing halo shape and orientation effects in large-scale structure.

Abstract

In the present work, we extend the classic halo model for the large-scale matter distribution including a triaxial model for the halo profiles and their alignments. In particular, we derive general expressions for the halo-matter cross correlation function. In addition, by numerical integration, we obtain instances of the cross-correlation function depending on the directions given by halo shape axes. These functions are called anisotropic cross-correlations. With the aim of comparing our theoretical results with the simulations, we compute averaged anisotropic correlations in cones with their symmetry axis along each shape direction of the centre halo. From these comparisons we characterise and quantify the alignment of dark matter haloes on the $\Lambda$CDM context by means of the presented anisotropic halo model. As our model requires multidimensional integral computation we implement a Monte Carlo method on GPU hardware which allows us to increase the precision of the results whereas it improves the performance of the computation.