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 is an element of [0, 3] 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 T(z) = T-0(1+z)(1-beta), the duality relation eta(z) and the cosmic opacity parameter T(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 ACDM model, thus confirming its predictive potential.