The manifestation of secondary bias on the galaxy population from IllustrisTNG300

Abstract

We use the improved IllustrisTNG300 magnetohydrodynamical cosmological simulation to revisit the effect that secondary halo bias has on the clustering of the central galaxy population. With a side length of 205 h−1 Mpc and significant improvements on the subgrid model with respect to previous Illustris simulations, IllustrisTNG300 allows us to explore the dependencies of galaxy clustering over a large cosmological volume and halo mass range. We show at high statistical significance that the halo assembly bias signal (i.e. the secondary dependence of halo bias on halo formation redshift) manifests itself on the clustering of the galaxy population when this is split by stellar mass, colour, specific star formation rate, and surface density. A significant signal is also found for galaxy size: at fixed halo mass, larger galaxies are more tightly clustered than smaller galaxies. This effect, in contrast to the rest of the dependencies, seems to be uncorrelated with halo formation time, with some small correlation only detected for halo spin. We also explore the transmission of the spin bias signal, i.e. the secondary dependence of halo bias on halo spin. Although galaxy spin retains little information about the total halo spin, the correlation is enough to produce a significant galaxy spin bias signal. We discuss possible ways to probe this effect with observations.

Quenching and morphological evolution due to circumgalactic gas expulsion in a simulated galaxy with a controlled assembly history

“…Sheth & Tormen 2004;Gao, Springel & White 2005). The existence of galaxy assembly bias in cosmological hydrodynamical simulations was first demonstrated, also using the EAGLE simulations, by Chaves-Montero et al (2016, see also Matthee et al 2017, and has also been demonstrated in the IllustrisTNG simulations by Montero-Dorta et al (2020). Efforts to detect the effect in observational surveys have yielded mixed results (see e.g.…”

The assembly bias of emission-line galaxies

“…Croton, Gao & White 2007;Zehavi et al 2018Zehavi et al , 2019Contreras et al 2019;Jiménez et al 2019;Xu, Zehavi & Contreras 2021) and hydrodynamical simulations (e.g. Artale et al 2018;Montero-Dorta et al 2020;Xu & Zheng 2020). Observational evidence for assembly bias remains inconclusive; some studies have suggested detections (e.g.…”

Mimicking the halo-galaxy connection using machine learning

“…The fact that we are able to predict the TNG300 clustering with precision, even when the sample is split in multiple ways, serves as a motivation to explore in more detail the effect of galaxy assembly bias, i.e., the dependence of the properties and clustering of galaxies on halo properties beyond halo mass 5 (see, e.g., Sheth & Tormen 2004;Gao et al 2005;Dalal et al 2008;Borzyszkowski et al 2017;Salcedo et al 2018;Sato-Polito et al 2019;Montero-Dorta et al 2020b;Tucci et al 2021;Montero-Dorta et al 2021b). On the cosmology front, as it has been already shown in the literature, ML can be used to generate high-fidelity mocks for upcoming surveys by applying the trained machine to N-body numerical simulations (e.g., Xu et al 2013;Zhang et al 2019;Yip et al 2019;Alves de Oliveira et al 2020;Kasmanoff et al 2020).…”

“…The TNG300 run followed the dynamical evolution of 2500 3 dark matter (DM) particles of mass 4.0 × 10 7 ℎ −1 M and (initially) 2500 3 gas cells of mass 7.6 × 10 6 ℎ −1 M . This box is a useful tool for galaxy formation and clustering science that has proven capable of reproducing a number of observational measurements (see, e.g., Springel et al 2018;Pillepich et al 2018a;Bose et al 2019;Beltz-Mohrmann et al 2020;Contreras et al 2020a;Gu et al 2020;Hadzhiyska et al 2020b;Montero-Dorta et al 2020b;Shi et al 2020;Hadzhiyska et al 2021;Montero-Dorta et al 2021a,b;Favole et al 2021).…”