Machine Learning meets the redshift evolution of the CMB Temperature

Abstract

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 $β$ parameter from the parametrisation $\text{T}(z)=\text{T}_0(1+z)^{1-β}$, the duality relation $η(z)$ and the cosmic opacity parameter $τ(z)$. Furthermore we place constraints on a temporal varying fine structure constant $α$, 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 $1σ$ region from the well established $Λ\text{CDM}$ model, thus confirming its predictive potential.