The dependence of halo bias on age, concentration and spin

TL;DR

This paper precisely measures how secondary properties like age, concentration, and spin influence halo bias across a wide mass range, revealing new regimes and improving bias measurement techniques.

Contribution

It provides the most precise measurement of secondary bias dependencies on halo properties and introduces a multi-tracer method to enhance bias detection accuracy.

Findings

No strong assembly bias above 10^{14} h^{-1} M_sun.

Halo concentration and spin affect bias up to cluster sizes.

Spin bias shows two regimes: increasing bias above 10^{11.5} h^{-1} M_sun, reversing below.

Abstract

Halo bias is the main link between the matter distribution and dark matter halos. In its simplest form, halo bias is determined by halo mass, but there are known additional dependencies on other halo properties which are of consequence for accurate modeling of galaxy clustering. Here we present the most precise measurement of these secondary-bias dependencies on halo age, concentration, and spin, for a wide range of halo masses spanning from 10$^{10.7}$ to 10$^{14.7}$ $h^{-1}$ M$_{\odot}$. At the high-mass end, we find no strong evidence of assembly bias for masses above M$_{vir}$ $\sim10^{14}$ $h^{-1}$ M$_{\odot}$. Secondary bias exists, however, for halo concentration and spin, up to cluster-size halos, in agreement with previous findings. For halo spin, we report, for the first time, two different regimes: above M$_{vir}\sim$10$^{11.5}$ $h^{-1}$ M$_{\odot}$, halos with larger values of spin have larger bias, at fixed mass, with the effect reaching almost a factor 2. This trend reverses below this characteristic mass. In addition to these results, we test, for the first time, the performance of a multi-tracer method for the determination of the relative bias between different subsets of halos. We show that this method increases significantly the signal-to-noise of the secondary-bias measurement as compared to a traditional approach. This analysis serves as the basis for follow-up applications of our multi-tracer method to real data.