English

Properties beyond mass for unresolved haloes across redshift and cosmology using correlations with local halo environment

Cosmology and Nongalactic Astrophysics 2022-11-23 v2 Astrophysics of Galaxies

Abstract

The structural and dynamic properties of the dark matter halos, though an important ingredient in understanding large-scale structure formation, require more conservative particle resolution than those required by halo mass alone in a simulation. This reduces the parameter space of the simulations, more severely for high-redshift and large-volume mocks which are required by the next-generation large sky surveys. Here, we incorporate redshift and cosmology dependence into an algorithm that assigns accurate halo properties such as concentration, spin, velocity, and spatial distribution to the sub-resolution haloes in a simulation. By focusing on getting the right correlations with halo mass and local tidal anisotropy α\alpha measured at 4×4 \times halo radius, our method will also recover the correlations of these small scale structural properties with the large-scale environment, i.e., the halo assembly bias at all scales greater than 5×5 \times halo radius. We find that the distribution of halo properties is universal with redshift and cosmology. By applying the algorithm to a large volume simulation (600h1Mpc)(600 h^{-1}{\rm Mpc}), we can access the 3050030-500 particle haloes, thus gaining an order of magnitude in halo mass and two to three orders of magnitude in number density at z=24z=2-4. This technique reduces the cost of mocks required for the estimation of covariance matrices, weak lensing studies, or any large-scale clustering analysis with less massive haloes.

Keywords

Cite

@article{arxiv.2112.15305,
  title  = {Properties beyond mass for unresolved haloes across redshift and cosmology using correlations with local halo environment},
  author = {Sujatha Ramakrishnan and Premvijay Velmani},
  journal= {arXiv preprint arXiv:2112.15305},
  year   = {2022}
}

Comments

9 figures, 6 tables, accepted by MNRAS, access to code at https://github.com/rsujatha/mockhaloprop