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arXiv 1208.0229astro-ph.CO
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局部偏差模型中的拉格朗日偏差

Lagrangian bias in the local bias model

Noemi Frusciante, Ravi K. Sheth

摘要:

通常假设晕斑涨落场可以表示为初始拉格朗日暗物质密度涨落场中的泰勒级数。我们表明,如果这种拉格朗日偏差是本地的,和初始条件是高斯的,那么两点之间的互相关晕和质量应该是线性成比例的质量-质量自关联函数。这一陈述在所有尺度上都是准确和有效的;没有更高阶的贡献,例如,从项成比例的产品或两点函数的卷积,其中一个可能会认为会出现在截断泰勒级数的晕偏置函数。此外,局部偏置示踪剂的自关联函数可以被写为质量的自关联函数中的泰勒级数;没有涉及例如,导数或卷积。此外,虽然自关联函数的首阶系数和线性偏置因子只是互相关函数的平方,但它与仅在大尺度范围内将平均晕数展开为局域密度的函数得到的结果相同。原则上,这些关系允许简单的测试是否晕偏差确实是当地的拉格朗日空间。我们讨论了为什么事情在实践中更复杂。我们还讨论了我们的研究结果,最近的工作重正化的晕偏见,证明这是更好的重正化比不。我们使用对数正态模型来说明我们的许多发现。

英文摘要:

It is often assumed that the halo-patch fluctuation field can be written as a Taylor series in the initial Lagrangian dark matter density fluctuation field. We show that if this Lagrangian bias is local, and the initial conditions are Gaussian, then the two-point cross-correlation between halos and mass should be linearly proportional to the mass-mass auto-correlation function. This statement is exact and valid on all scales; there are no higher order contributions, e.g., from terms proportional to products or convolutions of two-point functions, which one might have thought would appear upon truncating the Taylor series of the halo bias function. In addition, the auto-correlation function of locally biased tracers can be written as a Taylor series in the auto-correlation function of the mass; there are no terms involving, e.g., derivatives or convolutions. Moreover, although the leading order coefficient, the linear bias factor of the auto-correlation function is just the square of that for the cross-correlation, it is the same as that obtained from expanding the mean number of halos as a function of the local density only in the large-scale limit. In principle, these relations allow simple tests of whether or not halo bias is indeed local in Lagrangian space. We discuss why things are more complicated in practice. We also discuss our results in light of recent work on the renormalizability of halo bias, demonstrating that it is better to renormalize than not. We use the Lognormal model to illustrate many of our findings.

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