Nonparametric reconstruction of the cosmic expansion with local regression smoothing and simulation extrapolation

Abstract

In this work we present a nonparametric approach, which works on minimal assumptions, to reconstruct the cosmic expansion of the Universe. We propose to combine a locally weighted scatterplot smoothing method and a simulation-extrapolation method. The first one (Loess) is a nonparametric approach that allows to obtain smoothed curves with no prior knowledge of the functional relationship between variables nor of the cosmological quantities. The second one (Simex) takes into account the effect of measurement errors on a variable via a simulation process. For the reconstructions we use as raw data the Union2.1 Type Ia Supernovae compilation, as well as recent Hubble parameter measurements. This work aims to illustrate the approach, which turns out to be a self-sufficient technique in the sense we do not have to choose anything by hand. We examine the details of the method, among them the amount of observational data needed to perform the locally weighted fit which will define the robustness of our reconstruction. In view of our results, we believe that our proposal offers a promising alternative for reconstructing global trends of cosmological data when there is little intuition on the relationship between the variables and we also think it even presents good prospects to generate reliable mock data points where the original sample is poor.

13 pages, 6 figures, 2 tables; accepted in Phys. Rev. D

References in corpus (16)

• Wilkinson Microwave Anisotropy Probe (WMAP) Three Year Results: Implications for Cosmology
• Gaussian Process Cosmography
• Nonparametric Dark Energy Reconstruction from Supernova Data
• Nonparametric Reconstruction of the Dark Energy Equation of State
• Improved Constraints on the Acceleration History of the Universe and the Properties of the Dark Energy
• The oscillating dark energy: future singularity and coincidence problem
• Figure of Merit for Dark Energy Constraints from Current Observational Data
• Dipole of the luminosity distance: a direct measure of H(z)
• Using H(z) data as a probe of the concordance model
• Model Independent Reconstruction of the Expansion History of the Universe and the Properties of Dark Energy
• Reconstruction of the Dark Energy equation of state
• Crossing Statistic: Reconstructing the Expansion History of the Universe
• Bayesian Evidence for a Cosmological Constant using new High-Redshift Supernovae Data
• Model-independent determination of the cosmic expansion rate. I. Application to type-Ia supernovae
• A model-independent dark energy reconstruction scheme using the geometrical form of the luminosity-distance relation
• Revisiting a model-independent dark energy reconstruction method

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