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arXiv 2210.14239astro-ph.COastro-ph.IM
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利用Zel'dovich控制变量精确计算红移空间星系功率谱

Precision Redshift-Space Galaxy Power Spectra using Zel'dovich Control Variates

Joseph DeRose, Shi-Fan Chen, Nickolas Kokron, Martin White

摘要:

宇宙学中的数值模拟需要在体积、分辨率和运行时间之间进行权衡,这限制了可以模拟的宇宙的体积,导致在诸如功率谱或关联函数等总体平均量的预测中的样本方差。样本方差在大尺度上特别严重,这也是分析技术高度可靠的地方。这提供了一个机会,结合分析和数值技术的原则,以提高动态范围和聚类统计预测的可靠性。在本文中,我们扩展的技术Zel'dovich控制变量,以前证明了2点函数在实空间中,以减少样本方差测量的2点统计的有偏示踪剂在红移空间。我们证明,用这种技术,我们可以减少这些统计的样本方差下降到他们的散粒噪声限制到$k \sim 0.2\, h\rm Mpc^{-1}$。这允许与扰动模型更好地匹配,并改进对例如~类星体,星系和中性氢测量光谱红移调查在非常温和的计算费用。我们讨论了ZCV的实现,给出了一些例子,并提供预测的方法在各种条件下的功效。

英文摘要:

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.

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