Minimizing the stochasticity of halos in large-scale structure surveys

TL;DR

This paper investigates how to optimally weight halos by mass to minimize their stochasticity relative to dark matter in large-scale structure surveys, using simulations and analytical models.

Contribution

It extends previous work by identifying the optimal mass-dependent halo weighting scheme to reduce stochasticity, supported by simulation analysis and halo model predictions.

Findings

Optimal weighting matches linear mass weighting at high masses

Diagonalization reveals one dominant eigenmode for stochasticity reduction

Analytical halo model reproduces simulation results and suggests further reduction at lower masses

Abstract

In recent work (Seljak, Hamaus and Desjacques 2009) it was found that weighting central halo galaxies by halo mass can significantly suppress their stochasticity relative to the dark matter, well below the Poisson model expectation. In this paper we extend this study with the goal of finding the optimal mass-dependent halo weighting and use $N$-body simulations to perform a general analysis of halo stochasticity and its dependence on halo mass. We investigate the stochasticity matrix, defined as $C_{ij}\equiv<(\delta_i -b_i\delta_m)(\delta_j-b_j\delta_m)>$, where $\delta_m$ is the dark matter overdensity in Fourier space, $\delta_i$ the halo overdensity of the $i$-th halo mass bin and $b_i$ the halo bias. In contrast to the Poisson model predictions we detect nonvanishing correlations between different mass bins. We also find the diagonal terms to be sub-Poissonian for the highest-mass halos. The diagonalization of this matrix results in one large and one low eigenvalue, with the remaining eigenvalues close to the Poisson prediction $1/\bar{n}$, where $\bar{n}$ is the mean halo number density. The eigenmode with the lowest eigenvalue contains most of the information and the corresponding eigenvector provides an optimal weighting function to minimize the stochasticity between halos and dark matter. We find this optimal weighting function to match linear mass weighting at high masses, while at the low-mass end the weights approach a constant whose value depends on the low-mass cut in the halo mass function. Finally, we employ the halo model to derive the stochasticity matrix and the scale-dependent bias from an analytical perspective. It is remarkably successful in reproducing our numerical results and predicts that the stochasticity between halos and the dark matter can be reduced further when going to halo masses lower than we can resolve in current simulations.