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Comparing multi-field primordial feature models with the Planck data
Journal of Cosmology and Astroparticle Physics ( IF 5.3 ) Pub Date : 2021-06-01 , DOI: 10.1088/1475-7516/2021/06/005
Matteo Braglia 1, 2 , Xingang Chen 3 , DhirajKumar Hazra 4, 5
Affiliation  

In this paper, we use a complete model of classical primordial standard clocks as an example to develop a methodology of directly comparing numerical predictions from complicated multi-field feature models with the Planck data, including the Planck 2018 Plik unbinned likelihood and the statistically most powerful CamSpec 2020 likelihood for temperature and polarization data. As this two-field inflationary model offers a plethora of primordial feature spectra that represent combinations of sharp and resonant feature signals non-trivially distributed over extended cosmological scales, its data comparison has not been satisfactorily addressed by previous attempts using analytical templates. The method of this paper, consisting of numerical prediction, effective parameter construction and nested sampling data comparison, allows us to efficiently explore every possible spectra from the model. We classify the resulting feature candidates in three different frequency ranges. We use the Bayesian evidences to assess the statistical significance of the candidates over the baseline model, taking into account the effect of additional parameters and the look-elsewhere effect. Although none of the candidates is statistically significant, the methodology of this paper can be used to facilitate the future model-building and data-screening process of primordial features, and the candidates can be subjected to further tests with data from the upcoming cosmic microwave background polarization observations and galaxy surveys.



中文翻译:

将多场原始特征模型与普朗克数据进行比较

在本文中,我们以经典原始标准时钟的完整模型为例,开发了一种方法,将来自复杂多场特征模型的数值预测与普朗克数据直接进行比较,包括普朗克 2018 Plik unbinned似然和统计上最强大的CamSpec 2020 温度和极化数据的可能性。由于这种双场暴胀模型提供了过多的原始特征光谱,这些光谱代表了在扩展的宇宙学尺度上非平凡分布的尖锐和共振特征信号的组合,因此之前使用分析模板的尝试未能令人满意地解决其数据比较问题。本文的方法,包括数值预测、有效参数构建和嵌套采样数据比较,使我们能够有效地探索模型中所有可能的光谱。我们在三个不同的频率范围内对生成的候选特征进行分类。我们使用贝叶斯证据来评估候选人在基线模型上的统计显着性,同时考虑到附加参数的影响和别处效应。尽管没有一个候选者在统计上显着,但本文的方法可用于促进未来原始特征的模型构建和数据筛选过程,并且候选者可以接受来自即将到来的宇宙微波背景数据的进一步测试极化观测和星系调查。我们使用贝叶斯证据来评估候选人在基线模型上的统计显着性,同时考虑到附加参数的影响和别处效应。尽管没有一个候选者在统计上显着,但本文的方法可用于促进未来原始特征的模型构建和数据筛选过程,并且候选者可以接受来自即将到来的宇宙微波背景数据的进一步测试极化观测和星系调查。我们使用贝叶斯证据来评估候选人在基线模型上的统计显着性,同时考虑到附加参数的影响和别处效应。尽管没有一个候选者在统计上显着,但本文的方法可用于促进未来原始特征的模型构建和数据筛选过程,并且候选者可以接受来自即将到来的宇宙微波背景数据的进一步测试极化观测和星系调查。

更新日期:2021-06-01
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