Introducing Decorated HODs: modeling assembly bias in the galaxy-halo connection

TL;DR

This paper introduces decorated HODs, a flexible modeling framework that accounts for galaxy assembly bias, significantly affecting galaxy clustering and lensing predictions, and enhances the accuracy of galaxy-halo connection analyses.

Contribution

Decorated HODs extend traditional models by incorporating assembly bias with minimal parameter expansion, improving the modeling of galaxy clustering and lensing.

Findings

Assembly bias can double clustering at 200 kpc scales.

Assembly bias impacts large-scale clustering by about 15%.

Decorated HODs effectively quantify assembly bias effects.

Abstract

The connection between galaxies and dark matter halos is often inferred from data using probabilistic models, such as the Halo Occupation Distribution (HOD). Conventional HOD formulations assume that only halo mass governs the galaxy-halo connection. Violations of this assumption, known as galaxy assembly bias, threaten the HOD program. We introduce decorated HODs, a new, flexible class of models designed to account for assembly bias. Decorated HODs minimally expand the parameter space and maximize the independence between traditional and novel HOD parameters. We use decorated HODs to quantify the influence of assembly bias on clustering and lensing statistics. For SDSS-like samples, the impact of assembly bias on galaxy clustering can be as large as a factor of two on r ~ 200 kpc scales and ~15% in the linear regime. Assembly bias can either enhance or diminish clustering on large scales, but generally increases clustering on scales r <~ 1 Mpc. We performed our calculations with Halotools, an open-source, community-driven python package for studying the galaxy-halo connection (http://halotools.readthedocs.org). We conclude by describing the use of decorated HODs to treat assembly bias in otherwise conventional likelihood analyses.