The dependence of halo bias on age, concentration, and spin. Issue 2 (17th May 2019)

• Record Type: Journal Article
• Authors: Sato-Polito, Gabriela; Montero-Dorta, Antonio D; Abramo, L Raul; Prada, Francisco; Klypin, Anatoly
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 487:Issue 2(2019)
• Page Start: 1570
• Page End: 1579
• Publication Date: 2019-05-17
• Subjects: methods: numerical -- galaxies: formation -- galaxies: haloes -- dark matter -- large-scale structure of Universe -- cosmology: theory; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stz1338
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Halo bias is the main link between the matter distribution and dark matter haloes. 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 modelling 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⊙ . At the high-mass end, we find no strong evidence of assembly bias for masses above M vir ∼10 14 h −1 M⊙ . Secondary bias exists, however, for halo concentration and spin, up to cluster-size haloes, in agreement with previous findings. For halo spin, we report, for the first time, two different regimes: above M vir ∼ 10 11.5 h −1 M⊙, haloes 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 multitracer method for the determination of the relative bias between different subsets of haloes. 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 multitracer method to real data.