论文标题
使用EFT的可能性从有偏见的示踪剂中推断BAO量表
BAO scale inference from biased tracers using the EFT likelihood
论文作者
论文摘要
对应于重组时声级的Baryon声学振荡(BAO)的物理尺度,可以通过CMB实验确定。测量星系聚类中印记的BAO量表的表观大小使我们直接估计角直径距离和哈勃参数作为红移的函数。 BAO特征通过非线性结构形成抑制,从而降低了我们可以从标准星系聚类分析方法中推断BAO量表的精度。到目前为止,已经提出了许多通过所谓的BAO重建来消除这种阻尼的方法。但是,它们都依靠向后建模。在本文中,我们介绍了使用正向建模与EFT可能性结合的REST帧光环目录的BAO推断的第一个结果,如果固定了密度场的初始阶段。我们表明,当我们考虑$λ\ leq 0.25 \,h \,{\ rm mpc}^{ - 1} $的临界值$λ\ leq 0.25 \,{\ rm mpc}^{ - 1} $时,剩余的系统偏见小于2%,除了最有偏见的样本以外,所有的光环样本且与零以下且一致。我们还证明,与标准功率谱可能性方法相比,在固定相的相同假设下,与BAO量表的现场水平推理相关的1 $σ$错误是小的1.1至3.3倍,取决于截止值和晕圈样品的值。因此,我们的分析揭示了将现场水平推断用于高精度宇宙学的另一个有希望的特征。
The physical scale corresponding to baryon acoustic oscillations (BAO), the size of the sound horizon at recombination, is precisely determined by CMB experiments. Measuring the apparent size of the BAO scale imprinted in the clustering of galaxies gives us a direct estimate of the angular-diameter distance and the Hubble parameter as a function of redshift. The BAO feature is damped by non-linear structure formation, which reduces the precision with which we can infer the BAO scale from standard galaxy clustering analysis methods. Many methods to undo this damping via the so-called BAO reconstruction have so far been proposed; however, they all rely on backward modeling. In this paper, we present the first results of BAO inference from rest-frame halo catalogs using forward modeling combined with the EFT likelihood, in the case where the initial phases of the density field are fixed. We show that the remaining systematic bias is less than 2% when we consider cutoff values of $Λ\leq 0.25 \,h\,{\rm Mpc}^{-1}$ for all halo samples considered, and below 1% and consistent with zero for all but the most highly biased samples. We also demonstrate that, when compared to the standard power spectrum likelihood approach under the same assumption of fixed phases, the 1$σ$ errors associated to the field level inference of the BAO scale are 1.1 to 3.3 times smaller, depending on the value of the cutoff and the halo sample. Our analysis therefore unveils another promising feature of using field-level inference for high-precision cosmology.