Assembly bias from nuisance to probe
Year: 2026
Type: article
Abstract: Context. Two-halo galactic conformity is commonly interpreted as a manifestation of galaxy assembly bias, but its statistical structure and physical origin remain unclear. Aims. We aim to write the quenched-neighbour statistic in correlation-function form, to test whether its scale dependence follows the linear matter correlation function ξ mm lin ( r ), and to separate the contributions of halo-mass bias and assembly bias to its amplitude. Methods. Using galaxies in IllustrisTNG300-1 at z = 0, we measured the two-halo galactic conformity statistic of quenched neighbours at a distance, r , Δ f Q ( r ), and related quantities in real space. We computed the required correlations, performed shuffling tests at fixed halo mass, compared several Δ f observables, and explored the transformed family G n . Results. We explicitly demonstrate that Δ f Q ( r ) can be written directly in terms of correlation functions and that over ∼2 − 40 h −1 Mpc, it is well described by A fit ξ mm lin ( r ). Therefore, the non-linear and baryonic terms do not dominate the residual scale dependence isolated by this statistic. Halo-mass bias alone predicts lower amplitudes than measured, while the fixed-mass shuffling strongly suppresses the signal; in TNG300, the amplitude is therefore dominated by galaxy assembly bias at fixed halo mass. Quenching, colour, and concentration share a common rescaled shape, whereas stellar-mass and halo-mass splits do not. Conclusions. These results suggest that galaxy assembly bias sets the amplitude of two-halo conformity, while the double-difference structure of the statistic suppresses non-linear residuals when the compared populations have similar halo-mass and transition-scale structures.
Source: Astronomy and Astrophysics
Authors: Nelson Padilla, Ivan Lacerna, Dante Paz
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,865
OpenAlex ID: W7172437418
