Cosmic expansion history from SNe Ia data via information field theory -- the charm code

Abstract

We present charm (cosmic history agnostic reconstruction method), a novel inference algorithm that reconstructs the cosmic expansion history as encoded in the Hubble parameter $H(z)$ from SNe Ia data. The novelty of the approach lies in the usage of information field theory, a statistical field theory that is very well suited for the construction of optimal signal recovery algorithms. The charm algorithm infers non-parametrically $s(a)=\ln (\rho (a)/{\rho }_{\mathrm{crit}0})$, the density evolution which determines $H(z)$, without assuming an analytical form of $\rho (a)$ but only its smoothness with the scale factor $a=(1+z{)}^{-1}$. The inference problem of recovering the signal $s(a)$ from the data is formulated in a fully Bayesian way. In detail, we have rewritten the signal as the sum of a background cosmology and a perturbation. This allows us to determine the maximum a posteriory estimate of the signal by an iterative Wiener filter method. Applying charm to the Union2.1 supernova compilation, we have recovered a cosmic expansion history that is fully compatible with the standard $\Lambda$CDM cosmological expansion history with parameter values consistent with the results of the Planck mission.

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Cited by in corpus (8)

• Connecting early and late epochs by f(z)CDM cosmography
• Information theory for fields
• Reconstruction of the Dark Energy equation of state from latest data: the impact of theoretical priors
• Neural Network Reconstruction of $H^{\prime }(z)$ and its application in Teleparallel Gravity
• A model-independent characterisation of strong gravitational lensing by observables
• Generalised model-independent characterisation of strong gravitational lenses V: reconstructing the lensing distance ratio by supernovae for a general Friedmann universe
• Reconstruction of late-time cosmology using Principal Component Analysis
• A Non-parametric Reconstruction of the Hubble Parameter $H(z)$ Based on Radial Basis Function Neural Networks