The dependence of galaxy assembly bias on the selection criteria, number density, and redshift of galaxy samples

One of the key factors influencing galaxy clustering in the nonlinear regime is galaxy assembly bias, which describes the dependence of galaxy clustering on halo properties beyond halo mass. We studied this effect by analyzing galaxy samples selected according to stellar mass, luminosity, and broadband colors from the IllustrisTNG hydrodynamical simulation. We find that galaxy assembly bias depends strongly on the selection criteria, number density, and redshift of the galaxy sample; this effect increases or decreases galaxy clustering by as much as 25%. Interestingly, no single secondary halo property fully captures the strength of galaxy assembly bias for any galaxy population. Therefore, empirical models predicting galaxy assembly bias as a function of a single halo property cannot reproduce predictions from hydrodynamical simulations. Finally, we investigated how galaxy assembly bias arises from the interplay between halo assembly bias (the dependence of halo clustering on properties other than halo mass) and occupancy variation (the correlation between galaxy occupation and secondary halo properties). We provide a fast analytical expression to predict the level of galaxy assembly bias induced by any halo property in simulated galaxy catalogs without the need for computationally expensive shuffling techniques.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2026-01-15
DOI
https://doi.org/10.1051/0004-6361/202555093
Citations
1
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
23.83

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article

The dependence of galaxy assembly bias on the selection criteria, number density, and redshift of galaxy samples

J. Chaves-Montero, Sergio García-Moreno
1 citations
Astronomy and Astrophysics
Galaxies: Formation, Evolution, Phenomena
23.83
article

The dependence of galaxy assembly bias on the selection criteria, number density, and redshift of galaxy samples

J. Chaves-Montero, Sergio García-Moreno
article en
1 citations

Abstract

One of the key factors influencing galaxy clustering in the nonlinear regime is galaxy assembly bias, which describes the dependence of galaxy clustering on halo properties beyond halo mass. We studied this effect by analyzing galaxy samples selected according to stellar mass, luminosity, and broadband colors from the IllustrisTNG hydrodynamical simulation. We find that galaxy assembly bias depends strongly on the selection criteria, number density, and redshift of the galaxy sample; this effect increases or decreases galaxy clustering by as much as 25%. Interestingly, no single secondary halo property fully captures the strength of galaxy assembly bias for any galaxy population. Therefore, empirical models predicting galaxy assembly bias as a function of a single halo property cannot reproduce predictions from hydrodynamical simulations. Finally, we investigated how galaxy assembly bias arises from the interplay between halo assembly bias (the dependence of halo clustering on properties other than halo mass) and occupancy variation (the correlation between galaxy occupation and secondary halo properties). We provide a fast analytical expression to predict the level of galaxy assembly bias induced by any halo property in simulated galaxy catalogs without the need for computationally expensive shuffling techniques.

Astronomy and AstrophysicsVol. 706
Universidad Complutense de Madrid (ES), Institute for High Energy Physics (ES)
Centres de Recerca de Catalunya, European Commission, Generalitat de Catalunya, Institut de Física d'Altes Energies, HORIZON EUROPE Framework Programme, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
Openalex Percentile: Top 2%
Galaxies: Formation, Evolution, Phenomena
23.83
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The dependence of galaxy assembly bias on the selection criteria, number density, and redshift of galaxy samples — J. Chaves-Montero, Sergio García-Moreno · Astronomy and Astrophysics (2026) | TGRS Research Map | TGRS