Dissecting and Modelling Galaxy Assembly Bias

TL;DR

This paper investigates the causes of galaxy assembly bias using a semi-analytic model, identifying key halo properties that explain the bias and proposing a modified halo occupation distribution model to accurately reproduce it.

Contribution

The study identifies the primary halo properties responsible for galaxy assembly bias and introduces a practical modification to the halo occupation distribution model to incorporate this bias.

Findings

Matter density and tidal anisotropy can account for full GAB.

Common properties like halo age or concentration explain only 20-30% of GAB.

The modified HOD model accurately reproduces the level of GAB.

Abstract

Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes' assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a complex and challenging problem. Using a semi-analytic galaxy formation model, we study the individual contributions of different secondary halo properties to the GAB signal. These are obtained by comparing the clustering of stellar-mass selected samples to that of shuffled samples where the galaxies are randomly reassigned to haloes of fixed mass and a specified secondary halo property. We explore a large range of internal halo properties and environmental measures. We find that commonly used properties like halo age or concentration amount to only 20-30 per cent of the signal, while the smoothed matter density or the tidal anisotropy can account for the full level of GAB (though care should be given to the specific definition). For the "successful" measures, we examine the occupancy variations and the associated changes in the halo occupation function parameters. These are used to create mock catalogues that reproduce the full level of GAB. Finally, we propose a practical modification of the standard halo occupation distribution model, which can be tuned to any level of assembly bias. Fitting the parameters to our semi-analytic model, we demonstrate that the corresponding mock catalogue recovers the target level of GAB as well as the occupancy variations. Our results enable producing realistic mock catalogues and directly inform theoretical modelling of assembly bias and attempts to detect it in the Universe.