A non-parametric consistency test of the $\Lambda$CDM model with Planck CMB data

Abstract

Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base $\Lambda$CDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base $\Lambda$CDM model as cosmology's gold standard.

11 pages, 4 figures; v2: matches version published in JCAP

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• Gaussian Process Cosmography
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