论文标题
使用Zel'Dovich控制变体的精密红移空间星系功率谱
Precision Redshift-Space Galaxy Power Spectra using Zel'dovich Control Variates
论文作者
论文摘要
宇宙学中的数值模拟需要在数量，分辨率和运行时间之间进行权衡，以限制可以模拟的宇宙的体积，从而导致样本差异在集合平均数量的预测中，例如功率谱或相关函数（S）。样本方差在大尺度上特别急性，这也是分析技术可以高度可靠的地方。这提供了一个机会，以一种原则上的方式将分析和数值技术相结合，以提高聚类统计的预测的动态范围和可靠性。在本文中，我们扩展了Zel'Dovich Control变体的技术，以前证明了实际空间中的2分函数，以减少红移空间中有偏置示踪剂的两点统计的样本差异。我们证明，通过这种技术，我们可以将这些统计信息的样本差异降低到其射击限制至$ k \ sim 0.2 \，h \ rm mpc^{ - 1} $。这可以更好地与扰动模型和改进的预测，例如在光谱红移调查中以非常适中的计算费用进行测量的类星体，星系和中性氢的聚类。我们讨论ZCV的实施，提供一些示例，并为该方法在各种条件下的功效提供预测。
Numerical simulations in cosmology require trade-offs between volume, resolution and run-time that limit the volume of the Universe that can be simulated, leading to sample variance in predictions of ensemble-average quantities such as the power spectrum or correlation function(s). Sample variance is particularly acute at large scales, which is also where analytic techniques can be highly reliable. This provides an opportunity to combine analytic and numerical techniques in a principled way to improve the dynamic range and reliability of predictions for clustering statistics. In this paper we extend the technique of Zel'dovich control variates, previously demonstrated for 2-point functions in real space, to reduce the sample variance in measurements of 2-point statistics of biased tracers in redshift space. We demonstrate that with this technique, we can reduce the sample variance of these statistics down to their shot-noise limit out to $k \sim 0.2\, h\rm Mpc^{-1}$. This allows a better matching with perturbative models and improved predictions for the clustering of e.g.~quasars, galaxies and neutral Hydrogen measured in spectroscopic redshift surveys at very modest computational expense. We discuss the implementation of ZCV, give some examples and provide forecasts for the efficacy of the method under various conditions.
