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Large-scale sky surveys require companion large volume simulated mockcatalogs. To ensure precision cosmology studies are unbiased, the correlationsin these mocks between galaxy properties and their large-scale environmentsmust be realistic. Since galaxies are embedded in dark matter halos, animportant first step is to include such correlations -- sometimes calledassembly bias -- for dark matter halos. However, galaxy properties correlatewith smaller scale physics in halos which large simulations struggle toresolve. We describe an algorithm which addresses and largely mitigates thisproblem. Our algorithm exploits the fact that halo assembly bias is unchangedas long as correlations between halo property $c$ and the intermediate-scaletidal environment $\alpha$ are preserved. Therefore, knowledge of $\alpha$ issufficient to assign small-scale, otherwise unresolved properties to a halo ina way which preserves its large-scale assembly bias accurately. We demonstratethis explicitly for halo internal properties like formation history(concentration $c_{\rm 200b}$), shape $c/a$, dynamics $c_{v}/a_{v}$, velocityanisotropy $\beta$ and angular momentum (spin $\lambda$). Our algorithmincreases a simulation's reach in halo mass and number density by an order ofmagnitude, with improvements in the bias signal as large as 45% for 30-particlehalos, thus significantly reducing the cost of mocks for future weak lensingand redshift space distortion studies.

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