  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.
