Mock halo catalogs: assigning unresolved halo properties using correlations with local halo environment

TL;DR

This paper presents an algorithm that assigns unresolved small-scale halo properties in mock catalogs by leveraging correlations with the local tidal environment, improving bias accuracy for large-scale structure simulations.

Contribution

The authors introduce a novel method that preserves assembly bias by using intermediate-scale tidal environment correlations to assign small-scale halo properties.

Findings

Algorithm increases simulation reach in halo mass and density by tenfold.

Improves bias signal accuracy by up to 45% for small halos.

Reduces mock catalog generation costs for cosmological studies.

Abstract

Large-scale sky surveys require companion large volume simulated mock catalogs. To ensure precision cosmology studies are unbiased, the correlations in these mocks between galaxy properties and their large-scale environments must be realistic. Since galaxies are embedded in dark matter halos, an important first step is to include such correlations -- sometimes called assembly bias -- for dark matter halos. However, galaxy properties correlate with smaller scale physics in halos which large simulations struggle to resolve. We describe an algorithm which addresses and largely mitigates this problem. Our algorithm exploits the fact that halo assembly bias is unchanged as long as correlations between halo property $c$ and the intermediate-scale tidal environment $\alpha$ are preserved. Therefore, knowledge of $\alpha$ is sufficient to assign small-scale, otherwise unresolved properties to a halo in a way which preserves its large-scale assembly bias accurately. We demonstrate this explicitly for halo internal properties like formation history (concentration $c_{\rm 200b}$), shape $c/a$, dynamics $c_{v}/a_{v}$, velocity anisotropy $\beta$ and angular momentum (spin $\lambda$). Our algorithm increases a simulation's reach in halo mass and number density by an order of magnitude, with improvements in the bias signal as large as 45% for 30-particle halos, thus significantly reducing the cost of mocks for future weak lensing and redshift space distortion studies.