再见,本地物质密度偏差:本地质量密度对晕场统计的决定作用不大

Deaglan J. Bartlett, Matthew Ho and Benjamin D. Wandelt
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摘要

将暗物质场与晕的空间分布联系起来的偏差模型在当前的宇宙学分析中得到了广泛的应用。许多模型纯粹从局域欧拉物质密度预测晕,而微扰理论中的偏倚模型需要其他局域性质。我们评估了假设只有局部暗物质密度可以用模型无关的方式和在非摄动状态下预测晕的数量密度的有效性。利用n体模拟,我们研究了具有接近相等暗物质密度的空间体素被排列后光晕计数场的性质。如果局部物质密度(LIMD)偏倚是有效的,那么排列场和未排列场的统计性质将无法区分,因为它们都代表随机偏倚模型的公平绘制。如果拉格朗日半径大于体素大小的大约一半,并且对于质量小于~ 1015h−1M☉的晕,我们发现排列晕场具有尺度相关的偏差,在与当前调查相关的尺度上具有大于25%的功率。这些偏差模型通过不模拟相邻体素之间的相关性来消除小规模的功率,这大大提高了大规模的功率,以保存场的总方差。这个结论对于初始条件和宇宙学的选择是可靠的。因此,无论模型有多复杂,假设LIMD晕偏都不能再现晕在大尺度和晕质量范围内的分布。我们必须允许偏差是其他数量的函数,或者消除相邻体素在统计上独立的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bye-bye, Local-in-matter-density Bias: The Statistics of the Halo Field Are Poorly Determined by the Local Mass Density
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 N-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 ∼1015h−1M☉, 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.
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