Halo stochasticity in global clustering analysis

TL;DR

This paper investigates the stochastic relationship between dark matter and haloes using PCA on simulation data, revealing that stochasticity is significant at large scales and can be minimized by eigenvector-based bias modeling.

Contribution

It provides a detailed analysis of halo stochasticity at large scales, introducing the concept of minimally stochastic scenarios and demonstrating improved dark matter reconstruction using eigenvectors.

Findings

Stochasticity between haloes is non-negligible at large scales.

A dominant eigenvalue characterizes the minimally stochastic scenario.

Eigenvector-based bias improves dark matter distribution reconstruction.

Abstract

In the present work we study the statistics of haloes, which in the halo model determines the distribution of galaxies. Haloes are known to be biased tracer of dark matter, and at large scales it is usually assumed there is no intrinsic stochasticity between the two fields. Following the work of Seljak & Warren (2004), we explore how correct this assumption is and, moving a step further, we try to qualify the nature of stochasticity. We use Principal Component Analysis applied to the outputs of a cosmological N-body simulation to: (1) explore the behaviour of stochasticity in the correlation between haloes of different masses; (2) explore the behaviour of stochasticity in the correlation between haloes and dark matter. We show results obtained using a catalogue with 2.1 million haloes, from a PMFAST simulation with box size of 1000h^{-1}Mpc. In the relation between different populations of haloes we find that stochasticity is not-negligible even at large scales. In agreement with the conclusions of Tegmark & Bromley (1999) who studied the correlations of different galaxy populations, we found that the shot-noise subtracted stochasticity is qualitatively different from `enhanced' shot noise and, specifically, it is dominated by a single stochastic eigenvalue. We call this the `minimally stochastic' scenario, as opposed to shot noise which is `maximally stochastic'. In the correlation between haloes and dark matter, we find that stochasticity is minimized, as expected, near the dark matter peak (k ~ 0.02 h Mpc^{-1} for a LambdaCDM cosmology) and, even at large scales, it is of the order of 15 per cent above the shot noise. Moreover, we find that the reconstruction of the dark matter distribution is improved when we use eigenvectors as tracers of the bias. [Abridged]