论文标题

BARYON声学振荡的鲁棒性,用于早期修改为$λ$ CDM

Robustness of baryon acoustic oscillation constraints for early-Universe modifications to $Λ$CDM

论文作者

Bernal, José Luis, Smith, Tristan L., Boddy, Kimberly K., Kamionkowski, Marc

论文摘要

Baryon声学振荡(BAO)提供了强大的标准尺,可用于限制低红移时宇宙的扩张历史。标准的BAO分析返回了与模型无关的膨胀速率测量值,并且角直径距离作为红移的函数,并通过辐射阻力时的声层归一化。但是,该方法依赖于固定的,预计的模板(以给定的基准宇宙学获得)的各向异性距离失真,以适应观测值。因此,可能会扩展到共识$λ$ CDM为BAO功能增加贡献,而模板拟合无法捕获。假设宇宙学模型可以在重组之前修改扰动的生长,以测试标准BAO分析的鲁棒性,我们对计算的幂谱进行模拟BAO拟合。我们发现对所研究模型的BAO分析没有明显的偏见($λ$ CDM具有免费有效数量的相对论物种,早期的深色能量,以及在中微子和中微子之间具有相互作用的模型),即使对于\ textit {planck}测量的情况下,即使是良好的情况,也没有很好地拟合。该结果支持使用标准的BAO分析及其测量来执行宇宙学参数推断并限制外来模型。此外,我们还提供了一种用于对不同模型和调查的研究的方法,并讨论了处理BAO测量中最终偏见的不同选择。

Baryon acoustic oscillations (BAO) provide a robust standard ruler, and can be used to constrain the expansion history of the Universe at low redshift. Standard BAO analyses return a model-independent measurement of the expansion rate and the comoving angular diameter distance as function of redshift, normalized by the sound horizon at radiation drag. However, this methodology relies on anisotropic distance distortions of a fixed, pre-computed template (obtained in a given fiducial cosmology) in order to fit the observations. Therefore, it may be possible that extensions to the consensus $Λ$CDM add contributions to the BAO feature that cannot be captured by the template fitting. We perform mock BAO fits to power spectra computed assuming cosmological models which modify the growth of perturbations prior to recombination in order to test the robustness of the standard BAO analysis. We find no significant bias in the BAO analysis for the models under study ($Λ$CDM with a free effective number of relativistic species, early dark energy, and a model with interactions between neutrinos and a fraction of the dark matter), even for cases which do not provide a good fit to \textit{Planck} measurements of the cosmic microwave background power spectra. This result supports the use of the standard BAO analysis and its measurements to perform cosmological parameter inference and to constrain exotic models. In addition, we provide a methodology to reproduce our study for different models and surveys, as well as discuss different options to handle eventual biases in the BAO measurements.

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