[Paper Review] SHAM through the lens of a hydrodynamical simulation

Ginevra Favole, Antonio D. Montero-Dorta+4 more|arXiv (Cornell University)|Jan 26, 2021

Galaxies: Formation, Evolution, Phenomena7 references4 citations

TL;DR

This study enhances the sub-halo abundance matching (SHAM) framework by incorporating secondary subhalo properties—particularly starvation redshift (z_starve) and concentration at infall (c_infall)—to improve clustering predictions for red/blue and quenched/star-forming galaxies in the IllustrisTNG100 hydrodynamical simulation. The decorated SHAM model achieves strong agreement with simulation data, demonstrating that galaxy clustering depends significantly on environmental and accretion history proxies beyond halo mass alone.

ABSTRACT

We use the IllustrisTNG100 hydrodynamical simulation to study the dependence of the galaxy two-point correlation function on a broad range of secondary subhalo and galactic properties. We construct galaxy mock catalogues adopting a standard sub-halo abundance matching scheme coupled with a secondary assignment between galaxy colour or specific star formation rate and the following subhalo properties: starvation redshift z$_{ m starve}$, concentration at infall, overdensity $δ_R^{ m env}$, tidal anisotropy $α_R$, and tidal overdensity $δ_R$. The last two quantities allow us to fully characterise the tidal field of our subhaloes, acting as mediators between their internal and large-scale properties. The resulting mock catalogues overall return good agreement with the IllustrisTNG100 measurements. The accuracy of each model strongly depends on the correlation between the secondary galaxy and subhalo properties employed. Among all the subhalo proxies tested, we find that z$_{ m starve}$ and $c_{ m infall}$ are the ones that best trace the large-scale structure, producing robust clustering predictions for different samples of red/blue and quenched/star-forming galaxies.

Motivation & Objective

• To improve galaxy clustering predictions in SHAM by incorporating secondary subhalo properties beyond halo mass.
• To investigate how galaxy colour and specific star formation rate (sSFR) correlate with subhalo properties in a hydrodynamical simulation.
• To test whether environmental and tidal properties of subhaloes can better model the large-scale structure clustering of distinct galaxy populations.
• To validate the decorated SHAM framework against IllustrisTNG100 observations for multiple galaxy samples.
• To assess the relative performance of various subhalo proxies in reproducing observed clustering patterns across stellar mass bins.

Proposed method

• Constructed galaxy mock catalogues using standard sub-halo abundance matching (SHAM) with stellar mass and peak circular velocity (V_peak) as primary halo-galaxy proxies.
• Extended SHAM with secondary matching between galaxy properties (colour, sSFR) and subhalo properties: z_starve, c_infall, δ_R^env, α_R, and δ_R.
• Defined subhalo tidal overdensity δ_R and anisotropy α_R using the tidal tensor interpolated at subhalo positions and smoothed over 4 R_200.
• Measured galaxy two-point correlation functions in TNG100 and compared them to mock predictions across three stellar mass bins.
• Evaluated model accuracy by comparing mock clustering to fiducial TNG100 measurements, focusing on red/blue and quenched/star-forming sub-samples.
• Used the TNG100 database to extract subhalo properties and galaxy properties at z=0, enabling direct comparison of secondary correlations.

Experimental results

Research questions

• RQ1 Which subhalo secondary properties best reproduce the clustering of red and blue galaxies in the IllustrisTNG100 simulation?
• RQ2 How does the inclusion of environmental and tidal subhalo properties improve SHAM-based clustering predictions compared to standard mass-only SHAM?
• RQ3 To what extent do galaxy sSFR and colour correlate with subhalo accretion history and environmental conditions in a hydrodynamical context?
• RQ4 Can the decorated SHAM framework robustly predict clustering for low-density galaxy populations, such as quenched or high-sSFR galaxies?
• RQ5 What is the role of tidal anisotropy (α_R) in shaping galaxy clustering, and why does it show inconsistent performance across different density samples?

Key findings

• The starvation redshift (z_starve) and concentration at infall (c_infall) are the most effective subhalo proxies for predicting galaxy clustering across red/blue and quenched/star-forming samples.
• The tidal overdensity (δ_R) and subhalo environment overdensity (δ_R^env) also perform well, with slightly larger deviations from TNG100 data, indicating their relevance in clustering modeling.
• The tidal anisotropy (α_R) performs well for high-number-density populations but shows increased disagreement for low-density samples, suggesting limitations in its predictive power.
• Model accuracy strongly depends on the correlation between the chosen galaxy and subhalo secondary properties, with higher correlation yielding better clustering predictions.
• The decorated SHAM model achieves good agreement with TNG100 measurements across all tested galaxy samples, validating its use for multi-tracer cosmology.
• The results confirm that secondary halo bias—driven by environmental and accretion history—plays a key role in shaping galaxy clustering, and can be effectively modeled via subhalo proxies.