Cubic halo bias in Eulerian and Lagrangian space
Cubic halo bias in Eulerian and Lagrangian space
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DOI：
10.1088/1475-7516/2018/07/029
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发表时间：
2018-02
影响因子：
6.4
通讯作者：
Muntazir M. Abidi;T. Baldauf
中科院分区：
文献类型：
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作者：
Muntazir M. Abidi;T. Baldauf
次前导阶（即单环光晕功率谱）的预测取决于高达立方阶的局部和非局部偏置参数。线性偏置参数可以根据晕物质功率谱的大尺度极限来估计，二阶偏置参数可以根据大尺度、树级双谱来估计。三次算子自然会使用树级三谱来量化。由于后者的计算量很大，我们扩展了 Schmittfull 等人提出的二次场方法。 2014 到三次场，以估计三次偏置参数。我们将立方偏差算子的基组与晕场进行互相关，并用这些算子的互谱表示结果，以消除宇宙方差。我们获得了局部和非局部三次偏差参数的显着检测，这些参数部分与基于局部拉格朗日偏差方案的预测相矛盾。我们直接测量与我们的晕样本相关的原晕的拉格朗日偏差参数，并清楚地检测拉格朗日空间中的非局部二次项。我们没有发现低质量箱的非局部三次拉格朗日项的明确检测，但有一些温和的证据表明它们在最高质量箱中存在。虽然这里介绍的方法侧重于三次偏差参数，但该方法也可以应用于三次原初非高斯性的量化。
Predictions of the next-to-leading order, i.e. one-loop, halo power spectra, depend on local and non-local bias parameters up to cubic order. The linear bias parameter can be estimated from the large scale limit of the halo-matter power spectrum, and the second order bias parameters from the large scale, tree-level bispectrum. Cubic operators would naturally be quantified using the tree-level trispectrum. As the latter is computationally expensive, we extend the quadratic field method proposed in Schmittfull et al. 2014 to cubic fields, in order to estimate cubic bias parameters. We cross-correlate a basis set of cubic bias operators with the halo field and express the result in terms of the cross-spectra of these operators, in order to cancel cosmic variance. We obtain significant detections of local and non-local cubic bias parameters, which are partially in tension with predictions based on local Lagrangian bias schemes. We directly measure the Lagrangian bias parameters of the protohaloes associated with our halo sample and clearly detect a non-local quadratic term in Lagrangian space. We do not find a clear detection of non-local cubic Lagrangian terms for low mass bins, but there is some mild evidence for their presence for the highest mass bin. While the method presented here focuses on cubic bias parameters, the approach could also be applied to quantifications of cubic primordial non-Gaussianity.