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Abstract:

<jats:title>Abstract</jats:title> <jats:p>Bias models relating the dark matter field to the spatial distribution of halos are widely used in current cosmological analyses. Many models predict halos purely from the local Eulerian matter density, yet bias models in perturbation theory require other local properties. We assess the validity of assuming that only the local dark matter density can be used to predict the number density of halos in a model-independent way and in the nonperturbative regime. Utilizing <jats:italic>N</jats:italic>-body simulations, we study the properties of the halo counts field after spatial voxels with near-equal dark matter density have been permuted. If local-in-matter-density (LIMD) biasing were valid, the statistical properties of the permuted and unpermuted fields would be indistinguishable since both represent equally fair draws of the stochastic biasing model. If the Lagrangian radius is greater than approximately half the voxel size and for halos less massive than ∼10<jats:sup>15</jats:sup> <jats:italic>h</jats:italic> <jats:sup>−1</jats:sup> <jats:italic>M</jats:italic> <jats:sub>☉</jats:sub>, we find the permuted halo field has a scale-dependent bias with greater than 25% more power on scales relevant for current surveys. These bias models remove small-scale power by not modeling correlations between neighboring voxels, which substantially boosts large-scale power to conserve the field’s total variance. This conclusion is robust to the choice of initial conditions and cosmology. Assuming LIMD halo biasing cannot, therefore, reproduce the distribution of halos across a large range of scales and halo masses, no matter how complex the model. One must either allow the biasing to be a function of other quantities and/or remove the assumption that neighboring voxels are statistically independent.</jats:p>

Abstract:

<jats:p>Star formation is a key process that governs the baryon cycle within galaxies, however, the question of how it controls their growth remains elusive due to modeling uncertainties. To understand the impact of star formation models on galaxy evolution, we performed cosmological zoom-in radiation-hydrodynamic simulations of a dwarf dark matter halo, with a virial mass of <jats:italic>M</jats:italic><jats:sub>vir</jats:sub> ∼ 10<jats:sup>9</jats:sup> <jats:italic>M</jats:italic><jats:sub>⊙</jats:sub> at <jats:italic>z</jats:italic> = 6. We compared two different star formation models: a multi-freefall model combined with a local gravo-thermo-turbulent condition and a more self-consistent model based on a sink particle algorithm, where gas accretion and star formation are directly controlled by the gas kinematics. As the first study in this series, we used cosmological zoom-in simulations with different spatial resolutions and found that star formation is more bursty in the runs with the sink algorithm, generating stronger outflows than in the runs with the gravo-thermo-turbulent model. The main reason for the increased burstiness is that the gas accretion rates on the sinks are high enough to form stars on very short timescales, leading to more clustered star formation. As a result, the star-forming clumps are disrupted more quickly in the sink run due to more coherent radiation and supernova feedback. The difference in burstiness between the two star formation models becomes even more pronounced when the supernova explosion energy is artificially increased. Our results suggest that improving the modeling of star formation on small, sub-molecular cloud scales can significantly impact the global properties of simulated galaxies.</jats:p>