超越三角洲：调整标记统计数据以揭示修改后的重力
Beyond delta: Tailoring marked statistics to reveal modified gravity
摘要：
那些试图通过修正广义相对论（GR）来解释宇宙加速的模型，通过一种恢复、筛选机制来逃避严格的太阳系约束。对高密度、屏蔽区域进行降权，以有利于低密度、未屏蔽区域，这有可能提高这种修改后的重力模型所携带的信息量。在这项工作中，我们评估了一个新的“标记”的转换的性能，并进行了系统的比较，剪切和对数转换，在$Λ$ CDM和的背景下，$f(R)$和$f(R)$修改重力模型。性能是衡量的分数提升的Fisher信息和信噪比（SNR），这些模型相对于来自标准密度分布的统计数据。我们发现，所有这三个统计提供了改进的Fisher提升的基本密度统计。确定最佳增强信号和Fisher增强的“标记”和剪切变换的模型参数。我们还表明，标记是有用的傅立叶变换和实空间变换;一个显着的关联函数也提高了信噪比相对于标准的关联函数，并可以在轻度非线性尺度显示显着差异之间的$Λ$ CDM和修改后的重力模型。我们的研究结果表明，一系列简单的分析转换可以显着增加预测的信息提取的偏差从GR，从大规模的调查，并给出了一个潜在的检测前景更加可行。
英文摘要：
Models that seek to explain cosmic acceleration through modifications to General Relativity (GR) evade stringent Solar System constraints through a restoring, screening mechanism. Down-weighting the high density, screened regions in favor of the low density, unscreened ones offers the potential to enhance the amount of information carried in such modified gravity models. In this work, we assess the performance of a new "marked" transformation and perform a systematic comparison with the clipping and logarithmic transformations, in the context of $Λ$CDM and the symmetron and $f(R)$ modified gravity models. Performance is measured in terms of the fractional boost in the Fisher information and the signal-to-noise ratio (SNR) for these models relative to the statistics derived from the standard density distribution. We find that all three statistics provide improved Fisher boosts over the basic density statistics. The model parameters for the "marked" and clipped transformation that best enhance signals and the Fisher boosts are determined. We also show that the mark is useful both as a Fourier and real space transformation; a marked correlation function also enhances the SNR relative to the standard correlation function, and can on mildly non-linear scales show a significant difference between the $Λ$CDM and the modified gravity models. Our results demonstrate how a series of simple analytical transformations could dramatically increase the predicted information extracted on deviations from GR, from large-scale surveys, and give the prospect for a potential detection much more feasible.
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