Machine Learning meets the redshift evolution of the CMB Temperature
  We present a model independent and non-parametric reconstruction with a
Machine Learning algorithm of the redshift evolution of the Cosmic Microwave
Background (CMB) temperature from a wide redshift range $z\in \left[0,3\right]$
without assuming any dark energy model, an adiabatic universe or photon number
conservation. In particular we use the genetic algorithms which avoid the
dependency on an initial prior or a cosmological fiducial model. Through our
reconstruction we constrain new physics at late times. We provide novel and
updated estimates on the $\beta$ parameter from the parametrisation
$\text{T}(z)=\text{T}_0(1+z)^{1-\beta}$, the duality relation $\eta(z)$ and the
cosmic opacity parameter $\tau(z)$. Furthermore we place constraints on a
temporal varying fine structure constant $\alpha$, which would have signatures
in a broad spectrum of physical phenomena such as the CMB anisotropies. Overall
we find no evidence of deviations within the $1\sigma$ region from the well
established $\Lambda\text{CDM}$ model, thus confirming its predictive
potential.
